{ "cells": [ { "cell_type": "markdown", "id": "6561fac9", "metadata": {}, "source": [ "# Feedback & Regression\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 1, "id": "e540aa20", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets, linear_model\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "import pandas as pd\n", "sns.set_theme(font_scale=2,palette='colorblind')\n", "\n", "data_url = 'https://raw.githubusercontent.com/rhodyprog4ds/rhodyds/main/data/310_data_22-midsem.csv'" ] }, { "cell_type": "markdown", "id": "00ca27fa", "metadata": {}, "source": [ "## Feedback from you\n", "\n", "Here is the feedback from the survey that you completed with feedback on the course." ] }, { "cell_type": "code", "execution_count": 2, "id": "6d54ac50", "metadata": {}, "outputs": [], "source": [ "feedback_df_raw = pd.read_csv(data_url)" ] }, { "cell_type": "code", "execution_count": 3, "id": "43fc773f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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How much do you think you've learned so far this semester?How much of the material that's been taught do you feel you understand?How do you think the achievements you've earned so far align with your understanding?Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How well I follow instructions]Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [What I understand about the material]Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How much effort I put into assignments]How fair do you think the amount each of the following is reflected in the grading [How well I follow instructions]How fair do you think the amount each of the following is reflected in the grading [What I understand about the material]How fair do you think the amount each of the following is reflected in the grading [How much effort I put into assignments]Which of the following have you done to support your learning outside of class time?
044I think they reflect my understanding wellReflected perfectly in the gradingReflected perfectly in the gradingReflected perfectly in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingread the notes online, experimenting with the ...
154I think they reflect my understanding wellReflected perfectly in the gradingReflected perfectly in the gradingReflected perfectly in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingread the notes online, experimenting with the ...
233I think they reflect my understanding wellReflected moderately in the gradingReflected moderately in the gradingReflected moderately in the gradingShould be less reflected in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingread the notes online, download and run the no...
343I think they reflect my understanding wellReflected moderately in the gradingReflected moderately in the gradingReflected moderately in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingread the notes online, reading the documentati...
452I think they reflect my understanding wellReflected moderately in the gradingReflected strongly in the gradingReflected a little in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingShould be reflected more in the gradingread the notes online, experimenting with the ...
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" ], "text/plain": [ " How much do you think you've learned so far this semester? \\\n", "0 4 \n", "1 5 \n", "2 3 \n", "3 4 \n", "4 5 \n", "\n", " How much of the material that's been taught do you feel you understand? \\\n", "0 4 \n", "1 4 \n", "2 3 \n", "3 3 \n", "4 2 \n", "\n", " How do you think the achievements you've earned so far align with your understanding? \\\n", "0 I think they reflect my understanding well \n", "1 I think they reflect my understanding well \n", "2 I think they reflect my understanding well \n", "3 I think they reflect my understanding well \n", "4 I think they reflect my understanding well \n", "\n", " Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How well I follow instructions] \\\n", "0 Reflected perfectly in the grading \n", "1 Reflected perfectly in the grading \n", "2 Reflected moderately in the grading \n", "3 Reflected moderately in the grading \n", "4 Reflected moderately in the grading \n", "\n", " Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [What I understand about the material] \\\n", "0 Reflected perfectly in the grading \n", "1 Reflected perfectly in the grading \n", "2 Reflected moderately in the grading \n", "3 Reflected moderately in the grading \n", "4 Reflected strongly in the grading \n", "\n", " Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How much effort I put into assignments] \\\n", "0 Reflected perfectly in the grading \n", "1 Reflected perfectly in the grading \n", "2 Reflected moderately in the grading \n", "3 Reflected moderately in the grading \n", "4 Reflected a little in the grading \n", "\n", " How fair do you think the amount each of the following is reflected in the grading [How well I follow instructions] \\\n", "0 Is fairly reflected in the grading \n", "1 Is fairly reflected in the grading \n", "2 Should be less reflected in the grading \n", "3 Is fairly reflected in the grading \n", "4 Is fairly reflected in the grading \n", "\n", " How fair do you think the amount each of the following is reflected in the grading [What I understand about the material] \\\n", "0 Is fairly reflected in the grading \n", "1 Is fairly reflected in the grading \n", "2 Is fairly reflected in the grading \n", "3 Is fairly reflected in the grading \n", "4 Is fairly reflected in the grading \n", "\n", " How fair do you think the amount each of the following is reflected in the grading [How much effort I put into assignments] \\\n", "0 Is fairly reflected in the grading \n", "1 Is fairly reflected in the grading \n", "2 Is fairly reflected in the grading \n", "3 Is fairly reflected in the grading \n", "4 Should be reflected more in the grading \n", "\n", " Which of the following have you done to support your learning outside of class time? \n", "0 read the notes online, experimenting with the ... \n", "1 read the notes online, experimenting with the ... \n", "2 read the notes online, download and run the no... \n", "3 read the notes online, reading the documentati... \n", "4 read the notes online, experimenting with the ... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feedback_df_raw.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "2488d9f8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['How much do you think you've learned so far this semester?',\n", " 'How much of the material that's been taught do you feel you understand?',\n", " 'How do you think the achievements you've earned so far align with your understanding?',\n", " 'Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How well I follow instructions]',\n", " 'Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [What I understand about the material]',\n", " 'Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How much effort I put into assignments]',\n", " 'How fair do you think the amount each of the following is reflected in the grading [How well I follow instructions]',\n", " 'How fair do you think the amount each of the following is reflected in the grading [What I understand about the material]',\n", " 'How fair do you think the amount each of the following is reflected in the grading [How much effort I put into assignments]',\n", " 'Which of the following have you done to support your learning outside of class time? '],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feedback_df_raw.columns" ] }, { "cell_type": "code", "execution_count": 5, "id": "ea74a597", "metadata": {}, "outputs": [], "source": [ "short_names = {\"How much do you think you've learned so far this semester?\":'learned',\n", " \"How much of the material that's been taught do you feel you understand?\":'understand',\n", " \"How do you think the achievements you've earned so far align with your understanding?\":'achievements',\n", " 'Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How well I follow instructions]':'grading_instructions',\n", " 'Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [What I understand about the material]':'grading_understanding',\n", " 'Rank the following as what you feel the grading (when you do/not earn achievements) so far actually reflects about your performance in the class [How much effort I put into assignments]':'grading_effort',\n", " 'How fair do you think the amount each of the following is reflected in the grading [How well I follow instructions]':'fairness_instructions',\n", " 'How fair do you think the amount each of the following is reflected in the grading [What I understand about the material]':'fairness_understanding',\n", " 'How fair do you think the amount each of the following is reflected in the grading [How much effort I put into assignments]':'fairness_effort',\n", " 'Which of the following have you done to support your learning outside of class time? ':'learning_activities'}" ] }, { "cell_type": "code", "execution_count": 6, "id": "3252be5c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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044I think they reflect my understanding wellReflected perfectly in the gradingReflected perfectly in the gradingReflected perfectly in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingread the notes online, experimenting with the ...
154I think they reflect my understanding wellReflected perfectly in the gradingReflected perfectly in the gradingReflected perfectly in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingIs fairly reflected in the gradingread the notes online, experimenting with the ...
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" ], "text/plain": [ " learned understand achievements \\\n", "0 4 4 I think they reflect my understanding well \n", "1 5 4 I think they reflect my understanding well \n", "\n", " grading_instructions grading_understanding \\\n", "0 Reflected perfectly in the grading Reflected perfectly in the grading \n", "1 Reflected perfectly in the grading Reflected perfectly in the grading \n", "\n", " grading_effort fairness_instructions \\\n", "0 Reflected perfectly in the grading Is fairly reflected in the grading \n", "1 Reflected perfectly in the grading Is fairly reflected in the grading \n", "\n", " fairness_understanding fairness_effort \\\n", "0 Is fairly reflected in the grading Is fairly reflected in the grading \n", "1 Is fairly reflected in the grading Is fairly reflected in the grading \n", "\n", " learning_activities \n", "0 read the notes online, experimenting with the ... \n", "1 read the notes online, experimenting with the ... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feedback_df_cols = feedback_df_raw.rename(columns=short_names)\n", "feedback_df_cols.head(2)" ] }, { "cell_type": "code", "execution_count": 7, "id": "58335302", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2356/3116911913.py:3: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n", " learning_df = pd.get_dummies(learning_stacked).sum(level=0)\n" ] }, { "data": { "text/html": [ "
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attended Aiden's office hoursattended Dr. Brown's office hoursdownload and run the notesexperimenting with the code from classreading blogs or tutorials I find on my ownreading the documentation or course texttinkering with code to answer other aspects of the material that I'm curious aboutupdate the notes I took during class timewatching videos that I find on my owndownload and run the notesread the notes onlineupdate the notes I took during class time
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\n", "2 1 0 \n", "3 1 0 \n", "4 1 0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "learning_lists = feedback_df_cols['learning_activities'].str.split(',')\n", "learning_stacked = learning_lists.apply(pd.Series).stack()\n", "learning_df = pd.get_dummies(learning_stacked).sum(level=0)\n", "learning_df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "d108a1b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ " attended Aiden's office hours 7\n", " attended Dr. Brown's office hours 4\n", " download and run the notes 4\n", " experimenting with the code from class 11\n", " reading blogs or tutorials I find on my own 9\n", " reading the documentation or course text 11\n", " tinkering with code to answer other aspects of the material that I'm curious about 5\n", " update the notes I took during class time 4\n", " watching videos that I find on my own 7\n", "download and run the notes 1\n", "read the notes online 13\n", "update the notes I took during class time 1\n", "dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "learning_df.sum()" ] }, { "cell_type": "code", "execution_count": 9, "id": "7aa97c95", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-10-24_10_0.png" } }, "output_type": "display_data" } ], "source": [ "el_meaning = {1: 'much less',\n", " 2: 'a little less',\n", " 3: 'about right',\n", " 4: 'a little more ',\n", " 5: 'much more'}\n", "\n", "question_text = list(short_names.keys())[list(short_names.values()).index('learned')]\n", "el_counts,_ = np.histogram(feedback_df_cols['learned'],bins = [i+.5 for i in range(6)])\n", "el_df = pd.DataFrame(data = el_counts,index = el_meaning.values(),columns= [question_text],)\n", "\n", "# el_df.rename_axis(index='amount relative to expectations',inplace=True)\n", "el_df.plot.bar(legend=False);\n", "# sns.displot(feedback_df['learned'].replace(el_meaning))" ] }, { "cell_type": "code", "execution_count": 10, "id": "ba6b39a1", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-10-24_11_0.png" } }, "output_type": "display_data" } ], "source": [ "u_meaning = {0:'None', 1:'A little',3:'A moderate amount', 4:'Almost all',5:'All'}\n", "\n", "\n", "question_text = list(short_names.keys())[list(short_names.values()).index('understand')]\n", "u_counts,_ = np.histogram(feedback_df_cols['understand'],bins = [i+.5 for i in range(6)])\n", "u_df = pd.DataFrame(data = el_counts,index = u_meaning.values(),columns= [question_text],)\n", "\n", "\n", "u_df.plot.bar(legend=False);" ] }, { "cell_type": "markdown", "id": "e8273236", "metadata": {}, "source": [ "# Responses to Qualitative Comments\n", "\n", "- Going forward, the assignments are more tied together so you do not need to find as many datasets. I've made myself a note to go back and update A1-A5 for next year.\n", "- [help by contirbuting recommended datasets](https://github.com/rhodyprog4ds/rhodyds)\n", "- help by contributing do not use list to course website\n", "- I will try to make sure that most code is prepared so I can send on prismia\n", "- I recommend aligning your prismia & notebook side by side. (I cannot model this because of font size limitations)\n", "- I've requested for longer blocks for next fall... but classroom availability (this is a good idea!)\n", "\n", "\n", "## Regression\n", "\n", "\n", "\n", "We're going to predict **tip** from **total bill** using 80% of the data for training.\n", "This is a regression problem because the target, *tip* is a continuous value,\n", "the problems we've seen so far were all classification, species of iris and the\n", "character in that corners data were both categorical. \n", "\n", "Using linear regression is also a good choice because it makes sense that the tip\n", "would be approximately linearly related to the total bill, most people pick some\n", "percentage of the total bill. If we our prior knowledge was that people\n", "typically tipped with some more complicated function, this would not be a good\n", "model." ] }, { "cell_type": "code", "execution_count": 11, "id": "8ef34ebe", "metadata": {}, "outputs": [], "source": [ "tips_df = sns.load_dataset(\"tips\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "2dfa5af2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips_df.head()" ] }, { "cell_type": "markdown", "id": "5471f65f", "metadata": {}, "source": [ "Split the data so that we use 80% of the data to train a modle to predict the tip from the total bill" ] }, { "cell_type": "code", "execution_count": 13, "id": "1705a816", "metadata": {}, "outputs": [], "source": [ "tips_X = tips_df['total_bill'].values\n", "tips_X = tips_X[:,np.newaxis] # add an axis\n", "tips_y = tips_df['tip']\n", "\n", "tips_X_train,tips_X_test, tips_y_train, tips_y_test = train_test_split(tips_X,tips_y,train_size=.8)" ] }, { "cell_type": "markdown", "id": "daf95a88", "metadata": {}, "source": [ "To see what that new bit of code did, we can examine the shapes:" ] }, { "cell_type": "code", "execution_count": 14, "id": "4af9249b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(244, 1)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips_X.shape" ] }, { "cell_type": "markdown", "id": "81c472fb", "metadata": {}, "source": [ "what we ended up is 2 dimensions (there are two numbers) even though the second\n", "one is 1." ] }, { "cell_type": "code", "execution_count": 15, "id": "bdbe1a8c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(244,)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips_df['total_bill'].values.shape" ] }, { "cell_type": "markdown", "id": "84097aec", "metadata": {}, "source": [ "this, without the `newaxis` is one dimension, we can see that because there is\n", "no number after the comma. \n", "\n", "Now that our data is ready, we create the linear regression estimator object" ] }, { "cell_type": "code", "execution_count": 16, "id": "97b2aef2", "metadata": {}, "outputs": [], "source": [ "regr = linear_model.LinearRegression()" ] }, { "cell_type": "code", "execution_count": 17, "id": "7fa980ec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sklearn.linear_model._base.LinearRegression" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(regr)" ] }, { "cell_type": "markdown", "id": "ba26fe0d", "metadata": {}, "source": [ "Now we fit the model." ] }, { "cell_type": "code", "execution_count": 18, "id": "0ab659fb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression()" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr.fit(tips_X_train,tips_y_train)" ] }, { "cell_type": "markdown", "id": "351e7cd9", "metadata": {}, "source": [ "We can examine the coefficients and intercept." ] }, { "cell_type": "code", "execution_count": 19, "id": "b0256204", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0.10123819]), 0.9629915620718386)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr.coef_, regr.intercept_" ] }, { "cell_type": "markdown", "id": "92adebdb", "metadata": {}, "source": [ "These define a line (y = mx+b) coef is the slope.\n", "\n", "\n", "```{important}\n", "This is what our model *predicts* the tip will be based on the past data. It is\n", "important to note that this is not what the tip *should* be by any sort of\n", "virtues. For example, a typical normative rule for tipping is to tip 15% or 20%.\n", "the model we learned, from this data, however is ~%10 + $1. (it's actually\n", "9.68% + $1.028)\n", "```\n", "\n", "To interpret this, we can apply it for a single value. We trained this to\n", "predict the tip from the total bill. So, we can put in any value that's a\n", "plausible total bill and get the predicted tip.\n", "\n", "\n", "We can predict the data" ] }, { "cell_type": "code", "execution_count": 20, "id": "2272a09a", "metadata": {}, "outputs": [], "source": [ "tips_y_pred = regr.predict(tips_X_test)" ] }, { "cell_type": "code", "execution_count": 21, "id": "78da6eb2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(tips_y_pred)" ] }, { "cell_type": "markdown", "id": "bc5be465", "metadata": {}, "source": [ "To visualize in more detail, we'll plot the data as black points and the\n", "predictions as blue points. To highlight that this is a perfectly linear\n", "prediction, we'll also add a line for the prediction." ] }, { "cell_type": "code", "execution_count": 22, "id": "9a248214", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-10-24_32_1.png" } }, "output_type": "display_data" } ], "source": [ "plt.scatter(tips_X_test,tips_y_test, color='black')\n", "plt.scatter(tips_X_test,tips_y_pred, color='blue')" ] }, { "cell_type": "markdown", "id": "a4688ef1", "metadata": {}, "source": [ "``\n", "## Evaluating Regression - Mean Squared Error\n", "\n", "From the plot, we can see that there is some error for each point, so accuracy\n", "that we've been using, won't work. One idea is to look at how much error there\n", "is in each prediction, we can look at that visually first.\n", "\n", "\n", "\n", "These red lines are the {term}`residuals`." ] }, { "cell_type": "code", "execution_count": 23, "id": "901e5b4c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-10-24_34_0.png" } }, "output_type": "display_data" } ], "source": [ "plt.scatter(tips_X_test, tips_y_test, color='black')\n", "plt.plot(tips_X_test, tips_y_pred, color='blue', linewidth=3)\n", "\n", "# draw vertical lines frome each data point to its predict value\n", "[plt.plot([x,x],[yp,yt], color='red', linewidth=3)\n", " for x, yp, yt in zip(tips_X_test, tips_y_pred,tips_y_test)];" ] }, { "cell_type": "markdown", "id": "680de9c6", "metadata": {}, "source": [ "We can use the average length of these red lines to capture the error. To get\n", "the length, we can take the difference between the prediction and the data for\n", "each point. Some would be positive and others negative, so we will square each\n", "one then take the average." ] }, { "cell_type": "code", "execution_count": 24, "id": "2a4e48ad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8421650416284583" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_squared_error(tips_y_test,tips_y_pred)" ] }, { "cell_type": "markdown", "id": "406ec196", "metadata": {}, "source": [ "We can get back to the units being dollars, by taking the square root." ] }, { "cell_type": "code", "execution_count": 25, "id": "732f2522", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9176955059432613" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(mean_squared_error(tips_y_test, tips_y_pred))" ] }, { "cell_type": "markdown", "id": "8e7c9638", "metadata": {}, "source": [ "This is equivalent to using absolute value instead" ] }, { "cell_type": "code", "execution_count": 26, "id": "9b8565c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7208537516784601" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(np.abs(tips_y_test - tips_y_pred))" ] }, { "cell_type": "markdown", "id": "32329654", "metadata": {}, "source": [ "## Evaluating Regression - R2\n", "\n", "We can also use the $R^2$ score, the [coefficient of determination](https://scikit-learn.org/stable/modules/model_evaluation.html#r2-score).\n", "\n", "If we have the following:\n", "- $n$ `=len(y_test)``\n", "- $y$ `=y_test`\n", "- $y_i$ `=y_test[i]`\n", "- $\\hat{y}$ = `y_pred`\n", "- $\\bar{y} = \\frac{1}{n}\\sum_{i=0}^n y_i$ = `sum(y_test)/len(y_test)`\n", "\n", "$$ R^2(y, \\hat{y}) = 1 = \\frac{\\sum_{i=0}^n (y_i - \\hat{y}_i)^2}{\\sum_{i=0}^n (y_i - \\bar{y}_i)^2} $$" ] }, { "cell_type": "code", "execution_count": 27, "id": "a5b47087", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5244799162296381" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_score(tips_y_test, tips_y_pred)" ] }, { "cell_type": "markdown", "id": "0697a99f", "metadata": {}, "source": [ "This is a bit harder to interpret, but we can use some additional plots to\n", "visualize.\n", "This code simulates data by randomly picking 20 points, spreading them out\n", "and makes the “predicted” y values by picking a slope of 3. Then I simulated various levels of noise, by sampling noise and multiplying the same noise vector by different scales and adding all of those to a data frame with the column name the r score for if that column of target values was the truth.\n", "\n", "Then I added some columns of y values that were with different slopes and different functions of x. These all have the small amount of noise.\n", "\n", "````{margin}\n", "```{tip}\n", "[Facet Grids](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html) allow more customization than the figure level plotting functions\n", "we have used otherwise, but each of those combines a FacetGrid with a\n", "particular type of plot.\n", "```\n", "````" ] }, { "cell_type": "code", "execution_count": 28, "id": "f672a245", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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m3DffLFPduvVcMCIAgCeZP3+2Jk/+wGbcTz9tVZkyoS4YkXuhiAgAsGr27M/1ySeTbcYtXbpaYWE1XTAiAICjBPt5u2zlZEtefPEZ7d69y2bczz//5pErXQIAnMdgMKhRo9o246pWra4lS1bRR9cGiogAgGx69eqmyMhjNuO2bv1dQUFBLhgRAMAZ/I1Gp66cbE5aWpoaN65jM65evTs1b95C+fj4OHwMAADPFR0drXbt7rYZ9/jjT+qNN0a6YESegyIiAEBJSUlq1uxOu2L37j3CAR0AOEmyl5dpBeOSAb4qmYcVjO3l6JWTzfnrr7N6+OH7bcbVq3envv56icNeFwBQOPz0048aNuwVm3EvvdRf/fsPdv6APBRFRAAopCIj/1SvXl1txrVs2UozZsxywYgAoHCL8/JS35URWVZLzpgRGOjkQmJeVk62ZNWq5RozxvYMj9dee11PP/18nl8PAFC4vPbaIP38808247788ivddVdTF4zI81FEBIBC5Kuv5ujjjyfZjHv77bHq0aOXC0YEAJDSZyDeXECUpA2RUeq38rBmO+nSYkfr0+dJ7d27x2YcC6MAAHKjQYNadsXRR9c5KCICgIfr3r2TTp8+ZTNu7twFatiwsQtGBAC4WWyKIVsBMcOGyCjFphgU6uvl4lHZx94Dus2bdygkJMTJowEAeJIbNxLUokUju2L37ftD3t7eTh5R4UYREQA8jNFoVMOGd9gV++uvexQYGOjkEQEAbLmWmGrzcdOlxvns2rVrat3avsvC9u8/Ki+vgln8BIDCKD967+bUkSMReuKJR2zGlSkTqp9+2uqCESEDRUQA8AD//ntF7dvfY1fsgQO2V10GALhWUID1P8ttPe5su3fv0osvPmMzrmbNO7RkySoXjAgAkFP52XvXli+++FSffjrNZlz//oP10kv9XTAimOOQv0bef/99PfLII6pataojNgcAsMPWrZs1eHA/m3HNm9+tzz6b44IRAQByK9jPWx3DymhDZFS2xzqGlVGwn7dDFjvJiQ8/fE/ffDPfZtzw4SP12GNPumBEAIDcKoi9d++7r7Wioi7bjFuwYJnq1KGPbkHgkCLi3LlzNW/ePDVo0EC9evXSgw8+qICAAEdsGgCQydtvD9eaNd/ajHv33Yl66KFuTh8PAMAx/I1GzQyvq34rD2cpJGbMEHHVgV3jxnWUlpZmM27FirWqVq26C0YEAHCEgtB712AwqFGj2nbF7tixT0WLFnPqeJBzDr0u4sCBAzpw4IDGjx+vzp07q2fPnqpXz7XV4ri4OG3fvl27du3SH3/8oTNnzuj69esqUqSIQkNDVb9+fXXp0kWtWrWyuz/L2bNntXjxYm3btk0XL16UwWBQaGio7r77bvXq1Ut33GFf7zEAyA17G9avW7dRt99e3smjAQDkhbVeVIFGo2aH1zU9HhTgq2An96pKSUlRkyb2/b3++++H5O/v77SxAACcJ796754/f05dutxnVyxtlwo+hxQRu3btqg0bNujGjRuS0gt5S5cu1dKlSxUWFqZevXrpoYceUlBQkCNezqK5c+dq8uTJSkpKyvZYamqqTp8+rdOnT2v16tW666679MEHH+i2226zus0lS5ZowoQJSkxMzPLzM2fO6MyZM1qyZIn69++vgQMHOvR3AVB43bhxQy1aNLQrds+ew/L1pb0tALgDe3pR+RuNCvX1+u9AzgkFxOPH/9Qjj3S1K5YDOgDwDK7svbt06SJNmDDGZly7dvfp448/cdjrwvkc1hPx7bff1tq1a7VixQpFRETI+P9/8ERGRmrcuHGaNGmSOnbsqJ49e6pZs2aOeNlsTp8+bSogli1bVnfffbfq1KmjW265RUlJSTpw4IC+++47JSQkaM+ePXrqqae0dOlS3XLLLWa3t3r1ao0aNUqS5O3trU6dOqlFixby9fXVvn37tGrVKiUnJ+uTTz6Rv7+/XnrpJaf8XgA836FDB/T004/ZFcsBHQC4n/zuRTVr1meaMWOKzbh7722vKVNmOG0cAID84ezeu926PagzZ07bjJsw4QN16vRQrl8H+cvLaHT8XyuRkZFatmyZ1qxZo9jY2P9e7P8vH65QoYJ69uyp7t27q0yZMg573dGjR+v8+fN6/vnn1aJFC3l7e2eLuXDhgvr06aPTp9N37vDwcL333nvZ4qKjo3XfffcpLi5O3t7emj59utq3b58l5sCBA3r22Wd148YN+fr6as2aNQ5ZXCYtzaDo6Hj5+norJKS4YmLilZpqyPN2AWdjn80ZexvWd+jQUR9+aHulMuRMTvbXMmVKuGhUzpeRYyQ+s3Av7r6/Xk41qt5HWyw+HvG/Ng7vRXXPPU0UF3fdZhwHdM5BnnH/zy0Kl8Kwv8Z5eVnsvZub1Zntbbu0Zs0GVahQMcfbh3X5kWecUkTMkJKSop9++kkrVqzQjh07ZDCk/1IZxUQfHx+1bt1ajzzyiNq0aWO26JcTsbGxCg4Othl37Ngxde2afglH0aJFtWPHDhUtWjRLzAcffKAvv/xSkvTUU09p5MiRZrc1d+5cTZw4UZLUpUsXffTRR3n4DdJRRIS7Yp+1zd5EO23aTLVu3dbJoyncOLjjMwv34u7764m4FLWcsd3i49sHtFR1B/SisjfPbNiwRaGhZfP8erCMPOP+n1sULoVlf83cmzenvXfj4+PUsuVddsXu3XtEPj4+eRkqbMiPPOPURlp+fn7q1KmTOnXqpIsXL2rFihVauXKl/v77b0npfQo3b96szZs3q0yZMurevbt69uypChUq5Or17CkgSlKtWrVUpUoVnT59Wjdu3NDZs2dVq1bWP7h++OEH0+1nnnnG4rYeeeQRTZs2TQkJCfr555+VmJjIytQATIxGoxo2tG/xpS1bdqpkyWDnDggAkC+c1YsqJiZGbdu2sCt2//6jdi8sCADwTDntvbt79y69+KLlmkhmtF3yfC7rxl+uXDkNHDhQAwcO1Pbt27VixQpt3LhRycnJkqTLly/riy++0KxZs9S0aVM9/vjj6tChg9Mq14GBgabbNy/EcuLECV24cEGSVK1aNatFzcDAQDVu3Fjbtm1TQkKCfv/9d7Vu3dopYwbgHi5d+kf333+vXbEkWgAoHBzZi+qXX37WK6/0tyuWPAMAhUvmmYYlA3xVMgczDTOMGfO2Vq1aZjOua9dwjRkzIbdDhRvKlyU977rrLkVFRen06dM6evS/M6JGo1FGo1G7du3Srl27dPvtt2vIkCF66CHH9mhJTk7WmTNnTPdvXqE5MjLSdLtevXo2t1evXj1t27bN9FyKiEDhs2bNt3r77eE244KDg/XLLztdMCIAQEHibzRqZnhdi72obB3gvfLKAP3yyyabr/PMM3306qvD8jxeAID7iTOziJe9PQ/tbYfxxRfz1LRp8zyNE+7LpUXEiIgILV++XN9//73i4uIkpfdHNBqN8vPzU+PGjXXo0CElJCRIks6fP6/XX39dmzdv1kcffeSwyy/Wrl2r69fTm0zXqVMn2+Iup06dMt0uX768ze1ljslYsAWA53v66cd06NABm3GDB7+m559n9XYAKOwCjUbNDq9rdy8qew/ovv56qerVq+/IoQIA3EyymQKiJG2IjFK/lYc1+6YTVgaDQY0a1bZr27/+uifL1ZwovJxeRLx69apWr16t5cuX6/jx45LSZxxmqFSpknr16qXu3burVKlSunHjhtatW6cFCxbo6NGjMhqN+uGHH9SwYUM99dRTeR5PdHS0PvzwQ9P9fv36ZYvJKDBKUkhIiM1tZu7FmPm5ADyPvQd0K1asVbVq1Z08GgCAu7HWiyolJUVNmti+CkaSdu48QB9uAIBJbIohWwExw4bIKMWmGJRy6YI6d+5g1/ZohwFznFZE3L59u5YvX65NmzYpJSVF0n/FQ39/f91333169NFH1bRp0yzPK1q0qHr27KmePXvq66+/1vjx4yVJK1asyHMRMTk5WYMGDdK///4rSerQoYPuu+++bHEZMyElqUiRIja3m/kPuPj4+DyNMYOvr7d8fNJXq874P1DQeeI+m5iYqLvusm92x/79R+Tnl/eVNeEanri/2svXN+vvXhjfA7gfT91fjx+PVPfuXeyKPXw40nYQCgxP3WftQZ6BO3L3/fVaXJLZnxc7vlUl9yxUx0XWn1+hQkX98MNGJ4wMzpIf+6xDi4h///23Vq5cqZUrV+rixYuSss46rFq1qnr16qVu3brZtZLyU089pR9++EH79u3L0sMwNwwGg9566y3t2bNHklSxYkVNmFAwG4B6e3spJKS46X5QUNF8HA2Qc+6+z+7bt8/uXqwZizDBupiEZF2KS9LVG6kKLuqn0EB/hRTzLxCv6+77a07dnGOkwvcewL15wv76ySefaOLEiTbjmjRpom+//db5A4JTecI+mxPkGbg7d91fQ1IMptul142R37WLNp8zbtw4Pffcc84cFlzAlfusQ4qI33//vVasWKEdO3aYioYZ/y9SpIjuv/9+9erVS3fddVeOt12zZk3t27cv2wrKOWE0GjV69GitWbNGUvpCKnPnzlXJkiXNxhcrVsx0257XTUxMNN0uXry4lUj7GAxGXbuWIB8fbwUFFdW1azeUlmaw/UQgn7nzPvvBBxM1f/4cm3EdOnTUlCnTTfdjYhwz+9iTXTVIL6+I0E83LSTwWY96KunEk2a2Xjcn++vNB0PuLCPHSO79mUXh4+776913N9G1a1dtxr333gd66KGupvvkGfdFnnH/zy0KF3ffX9s3rKVydsR9//1Pqlixkuk+ecZ95UeecUgR8bXXXjMtkJKhRo0aeuSRR9StWzcFBQXlett5vTTQaDTqnXfe0dKlSyVJt956q+bPn291wZQSJUqYbsfExNh8jdjYWLPPzYvU1P92gLQ0Q5b7QEHnLvusvf0Np079VG3atDPdd4ffraBI9vLSyxYaPPddEZGtwbMrX7eY0v8d3WV/daSbf9/C+B7AfbnT/mpvnlm/fovKli1ruu8uvx/s4077rKOQZ+DO3GV/jY+PU8uW9k3U2rLvD5X0/u/svTv8frCfK/dZh13ObDQaFRAQoAcffFC9evVSw4YNHbLdLl266I477sj1mMaMGaPFixdLksqWLauvvvpKFStWtPq8qlWrmm6fP3/e5utkjqlSpUquxgrA+YxGoxo2tO/75Jdfdig42PbCSrDOngbPob5e+fK6xXx8HP66AAq32NgY3XtvC7ti9+8/Ki8vx3//AQA81549v+uFF562K3b5rxEKCvBVsJ+3U07ao3BySBGxZs2a6tWrlx5++GGHzcTLUL9+fdWvb9+iBpllFBAXLUrvHhoaGqqvvvpKlSpVsvFMKSwszHQ7IiLCZnzmmBo1auR4rACc59KlS7r//jZ2xbICmeMke3kpNsWg6BspWtOnqXaeidGUbacUn5yWJe5aYup/K5Q60LXEVJuP3xZAERFA3m3Z8rOGDOlvVyx5BgCQU+++O0orViy1Gffww901dux72R+ggAgHckgRcfXq1Y7YjMPcXEAsU6aMvvrqK1WuXNmu51evXl233Xab/v77b508eVLnz5+3ePlzfHy89u7dKyl9ZembV5sG4Hrr1n2nESNetxlXsmRJbdmyywUjKlzivLzU96ZLidvXKK2FTzZS72/2ZSkkBgU4dH0vu7frrNcFUDi89tog/fzzTzbjnn76Ob322hsuGBEAwJPY2w7j88/nqlkz+2bAA47gkUdRY8eOzVZAzOllxg8++KBmz54tSZo3b55GjhxpNm7p0qVKSEhvHNyuXTsVLeqeKzkB7u7ZZ3vrwIF9NuMGDXpVffq87IIRFU7JZgqIkrTpePr9V1pV1fhNxyWlL3IS7OftlLOjwX7e6hhWRhsyLaqSwfS6AJAD9h7Qff31EtWrd6eTRwMA8CQGg0GNGtW2K3bbtt0OvwIUsJfHFRHfffddLVy4UNJ/BcTMPQ7t1adPHy1evFjx8fFasGCBWrRoofbt22eJOXjwoKZOnSpJ8vX11YABA/L+CwCwm70HdMuXf6fq1cNsByLPrPUi3HT8ioa0Sv8+7hhWRjOdtKiKJPkbjZoZXlf9Vh7OUkjM+rr0IgNgWUpKipo0qWdX7M6dBxQQEODkEQEAPMmFC+fVuXMHu2Jph4GCwqOKiJMnT9Y333wjSfLy8tLTTz+tU6dO6dSpU1afV7t2bd12221ZfnbLLbfo7bff1vDhw2UwGDRw4EB16tRJLVu2lLe3t/bt26dvv/1WSUlJkqRBgwapWrVqzvnFAEiSEhMT1bx5A7tid++OyPPq7p4qo1/htcRUlQzwVUk7my3b8zxbvQhLBvgq4n9tXNLgOdBo1OzwuqYx01gaKLhy+73kaCdOHFfPng/ZFcsBHQAgp5YvX6Jx40bbjCtfvoLWrrXdNgNwNY8qIu7b99+ljEajUR999JFdz3vvvfcUHh6e7efdu3fXjRs3NHHiRCUlJWnt2rVau3ZtlhgfHx/17dtXffv2zdvgAZgVEXFITz3Vy65YDuhsM9evMGN2XqCVA3Z7n2er12Cpon7pqzG7qDjgbzQq1Nfrv8VbKCACBUqKl5fijdLAXHwvOcqcOV9o2rSPbcbVr99AX3212OnjAQB4lp49H9KJE8dtxr3xxgg9/vhTLhgRkHseVUR0ht69e+vuu+/W4sWLtW3bNl28eFFGo1GhoaFq3ry5Hn30UdWubV/vAgD2mTz5A82fP9tmXNu2HTR58nQXjMgzWOpXuCEySv1WHtZsC5cX5+R5dvUipJAHQOknJzad+FdLDv5t6puawdb3Ul7de29zxcbG2owbN+59denS1eGvDwDwbPa2XVq9+kdVqlTZuYMBHMijiohff/21U7ZbuXJlDR8+XMOHD3fK9gHYn2inTPlU997bzsmj8UzW+hVuPxOtBKMUm2rMdjmhtedtiIxSbIohfXah7O1FCKCwyzg5MeieqtkKiBlu/n7JK3vzzPr1W1S2bFmHvCYAoHBISIjX3Xc3tit2794j8vHxcfKIAOfwqCIiAPdhNBrVsOEddsX+8ssOBQeHOHlEns9Sv8Li/j5a+GQjDVxl/nLCuKQUm9s1XS4sehEChUFeexhmnJx4sXklq3E3f7/kRGxsjO69t4Vdsfv3H5WXF4stAQDst3fvbvXpY9/lx7RdgqegiAjAZS5fvqSOHdvYFUuidTxL/QpfaVVV07adtng54Sfd6+Z4u/QiBDxXbnurZpZxUiPA1/pMDFt9Vm+2detmDR7cz65Y8gwAIKfGj39Hy5bZ7o/bpUtXjRv3vgtGBLgWRUQATvX992v01lvDbMYFBpbQr7/udsGI3JMjVi611K+wWaUQjd9kvtnzhsgoJaUa6HMIQJLtHqlf9Kin68lpNr+rMoqDu87GqH2N0mYvabb3+2Xo0MHauHGDzbE/9dSz+t//aE0DAMgZe9thzJw5Wy1atHTyaID8RRERgMM980xv7d27x2bcgAGv6MUXWdncFkfM+pEs9yu05XpiKn0OAUiy3lt1Q2SUTkQnqMPnO00/s/RdlXFSY8q2U1r4ZCNJylJItPX9Yu8B3VdfLVb9+g3sigUAQJIMBoPdeWbbtt0qUaKEk0cEFBwUEQE4hL2Jdvny71S9epiTR+M5cruisiXm+hXaenZQgC99DgFIstxbNUP0jaw9VC19V2U+qdH7m316pVVVDWlVVZJUKaSoSt30/ZKSkqIGDerZNcYdO/araNGi9v5KAADowoXz6ty5g12xtMNAYUYREUCuJCUlqVmzO+2K3b07Qn5+uWuMX9A44rLinMjJysj2urlfYbKXl12XK9PnEICtHoXmehxa+q6yenLCaNTJkyfUo0cXu8bFAR0AIKdWrFiqd98dZTPu9tvLa926jS4YEVDwUUQEYLfDhyP05JOP2BXriQd0jrqsOCdszfrJy8qlGSxd5szlygBuZqm3qiS1r1Fau87GmH2epe+qm09OzJ0zS1OnfmhzHPXq3amvv16Sw9EDAAq7Xr26KjLyT5txw4a9pSeeeNoFIwLcC0VEAFa99dYwff/9Gptxbdq01YwZnyskpLhiYuKVmmpwwehcx9GXFdvL1qyfnK5cagmXKwOwh7WTDgPuqaze3+wz+zxr31X2tsMYP36inn32KY/MMQAA57E3z6xdu14NG9YlzwBWUEQEkI29iXby5Blq27a9k0dTMDjjsmJ7WJv14+iVkblcGYA9zJ10KOHvo8Grjyg+OS1bvLnvKnvzzPr1v6hs2VslSb6+3o75BQAAHi0uLk733HOXXbF79x6Rj096Kw7yDGAbRUQAMhqNatjwDrtiN2/eoZCQECePqOBxxWXF5nCpMYCCKNtJB4NBkx+urcRUg9nvqrh/r6hdu5Z2bXv//qPy8nL8SRkAgOf69detGjjwJbtiPbHtEuAqFBGBQurvvy+oUyf7ZhGSaF13WbE5XGoMwB3c/F2155cfNXFkX90zxvZzyTMAgJwaPLivtm79xWZcw4aNNXfuAucPCCgEKCIChcgPP6zVm28OtSu2MB/QmVuBOcTPvhWMnYVLjQG4g5HDXtGGDT/YjOvQ4X59+OFUF4wIAOBJ7G2H8cEHU3TffQ84eTRA4UMREfBwL7zwtPbs+d1mXK9evfXWW6NcMKKCzdoKzJ+F11VfLisGgCzsPaCbOXO2WrSw75JmAAAkyWAwqFGj2nbF/vLLDgUHF762S4ArUUQEPJC9B3Rff71U9erVd/Jo3Ic9KzBzWTGAwi4tLU2NG9exK/a33/aqWLHiTh4RAMCTREVd1n33tbYrtjBfPQXkB4qIgAdITk5W06b2FQN37z4kPz9/J4/IPdm7AjOXFQMobC5cOK/OnTvYFcsBHQAgp37+eaNee22gzbhy5W7TDz/87IIRATCHIiLgpk6dOqnw8M52xXJAZ5/8WoEZAAqitWtXa+TIN2zG1alTTwsWLHPBiAAAnuTNN4fqhx/W2owbOvRNPfnkM7l+HXP9zrmSCMgdioiAG1m8eIEmTnzXZtwTTzyjYcPedMGIPEt+rsAMALnlyIOjfv36aMeO7Tbjpk79VG3atMvVawAACi972y59//0m3Xbb7Xl6rRQvL8UbpYEW+p0HUkgEcowjYqCAe/zxcB09+ofNuFmz5qtJk2YuGJHnCvbzztcVmAEgp6wtBmXvwZG9B3SbNv2qW24pnatxAgAKp8TERDVv3sCu2L17j8jHx8chrxvn5aVNJ/7VkoN/a9Nxy/3OmZEI5AxFRKCAMRqNatjwDrtit27dpaCgkk4eUeHhbzRqZnhd9WMFZgBuwJ7FoMx9b8XFxemee+6y6zX27z8qLy8vh4wXAFA4HDt2VI891t1mXMmSJbVlyy6Hv35Gfhx0T9VsBcQMmfudA7AfRUSgAIiJiVHbti3siqW/oXMFGo2swAzALdi7GJQkHTiwT88+29vmNqtUqapVq7536DgBAJ5v3rzZmjLlA5txL7zQVwMHvuLUsWTkxxebV7IaR79zIOcoIgL55LffflX//i/YjGvYsLHmzl3gghEhg7/RyArMAAo8W4tBzZz6kVZ9/aXN7bz66jA980wfRw0LAFBIPPzw/frrr7M24+bPX6Q772zoghGly8iPAb7WL42m3zmQc3xqABcaN260li9fYjNu9Ohx6t69pwtGZB9WNAOAgsfcwU/Z5a/IOyVRkrTKynOXLl2tsLCaThoZAMAT5aTt0vbte1S8eGCuXysvxx8Z+XHX2Ri1r1Ha7CXN9DsHcociIuBk9jasX736R1WqVNm5g8khVjQDgIIr2M9b91UvpcPv9rIrfteugypSpIiTRwUA8CRXrkSpQ4dWdsU6qu2StUXDikiKsVFczFgsccq2U1r4ZCNJylJIpN85kHsUEQEHS0lJVpMm9e2K3b07Qn5+zunDkdfZg6xoBgAF04UL59W5cwe7YumjCwDIqV9++VmvvNLfZlzLlq01Y8YXDn1tW4uG9axfTi8tP2T6ubnJDZkXS+z9zT690qqqhrSqKkmqFFJUpbiqCsg1ioiAA5w+fUrdu3eyK9YVB3TWzt7ZM3uQFc0AoGBZt+47jRjxus24Dp0e1oT3PuDgCACQIyNGvK51676zGTd+/Afq3Pkhp43D1qJhA++pku1n5iY3WF0skRwJ5BpFRCCXlixZqPfeG2szrnfvp/X662+5YETpbJ29+6JHPV1PTrM6Q5EVzQAg/w0Y8JK2b99qM27q1E/Vpk27/37AwREAwA72tl36/vtNuu222508mnS2Fg1LTE3L9jNLkxtYLBFwPIqIQA707t1Tf/xx2GbcrFnz1aRJMxeMKDtbZ+9ORCeow+c7TT8zN0ORFc0AIH/Ye0C3ceM2lS5dxsmjAQB4ksTERDVv3sCu2L17j8jHx/qxgDPYOr6wdHzC5AbANagAAFbkZAWyrVt3KSiopJNHZJuts3fRN1Ky3Dd3CQArmgEobPJrFfr4+Di1bHmXXbH79x+VlxctJAAA9vvzz2N69NFuNuNKlAjStm2/O39ANmQsirIhMirbY+1rlNauszFmn8fkBsA1+KQBN4mNjdG997awK7YgNqzPzdm7my8BYEUzAIVJXvvI5tSBA/v07LO9bcZVqlRZq1f/6PDXBwB4tvnzZ2vy5A9sxvXp87IGDXrVBSOyX+ZFUTIXEjuGldGb7aury+zshU4mNwCuQxERkLRjx3b169fHZlyDBo00b95CF4wo93J79i7zJQCsaAagsLDVR9ZRq9BPnz5FX375mc24V14ZpmeftZ2PAADIrGvXB3T27BmbcfPnL9KddzZ0/oDywNKiKCmSWlYula24yOQGwHUoIqLQGj9+jJYtW2QzbtSodxUe/ogLRuQY1s7eDbinsnp/s8/s826ewciKZgAKA1t9ZPOyCn2rVk11/fo1m3FLlnyrmjXt64UIAICUs7ZL27fvUfHigU4ekWOZWxTFX7J8fALAJSgiolCxt2H96tU/qlKlys4djBOZKwCW8PfR4NVHFJ+cfUUzS5cAsKIZAE9nq49sThq1p6WlqXHjOnbF7tx5QAEBAXbFAgAgSVeuRKlDh1Z2xRbEtkuOwPEJkL8oIsKjpaQkq0mT+nbF7t4dIT8/z1nRK1uCNRg0+eHaSkw1cAkAAPw/W31kbT3+998X1KlTe7tey1MP6AAAzrN162YNHtzPZlzLlq00Y8YsF4wIQGFGEREe58yZU+rWrZNdsYXtgM7qJcoAUAhZ6yNraZb299+v0VtvDbO57U6dHtKECbYb2wMAkNnIkW9o7drVNuPGj/9AnTs/5IIRAUA6iojwCEuXLtKECWNsxvXu/bRef/0tF4woq2QvL1PhrmSAr0rmY+GOSwAA4D/W+shmnqU9aNDL2rZti83tTZnyqe69t53TxgsA8Ez2tl1at26jbr+9vJNHAwDmUUSE23rqqUcVEXHQZtwXX8xT06bNXTAi8+LMrPyZcXAaSAEPAPKdpVnaTe+sadfzN27cptKlyzh5lAAAT5KUlKRmze60K3bv3iPy8fFx8ogAwDaKiHAbOVmBbOvWXQoKKunkEdmWbKaAKKWv+Nlv5WHNphchABQI/kajiifFq+M9d9kVv3//UXl55W7VZgBA4RQZ+ad69epqM6548eLavn2vC0YEADlDEREF2rVr19S6dVO7Ygtif8PYFEO2AmKGDZFRik0xKNSXg1AAcDR720gcO/aHHnss3Ob2KlSoqDVrNjhjqAAAD7ZixVK9++4om3F9+rysQYNedcGIACD3KCKiwNm/f6+ee+4Jm3ENGjTSvHkLXTCi3LuWmGrzcVNvQgCAQ9hqI/H113P10Ufv29zOK68M07PP9nHmUAEAHujll5/Trl07bMbNm7dQDRo0csGI8qYg9XcHkL8oIqJA+PTTafrii09txsW06KPEyk3UMayMpofXLfCLggQFWP+I2XocAJAzltpI7Js6RPeMOW/z+StWrFW1atWdNTwAgAfKSdul337bq2LFijt5RI5Df3cAmVHBQL6xdwWysMEztCUqayNhd+kpGOznrY5hZbKs+JmhY1gZBft5F/hCKAC4Ul5nO5jaSBgMKrekv13P2bXroIoUKZLbIQMACqF//72i9u3vsSu2ILZdsgf93QHcjCIiXCYlJUVNmtSzK3bPnsPy9fXV5VSj6n20xWyMO/QU9DcaNTO8rvqtPJylkJhx9o6kCwD/sTbboYikGBvFxYsX/9aDD7ZTORuv4+/vr99/P+T4XwAA4NG2bt2swYP72Yzr0qWrxo2z3TajoKO/O4CbUUSEU505c0rdunWyGVeu3G364Yefs/3cE3oKBhqNmh1e1zSzJijAV8H0EQFQCFmbZWhrtkPP+uX00vL/Cn8ZxcVt36/Rm28Otfna8WFtda3xo4r4XxsOeAAAdnv77eFas+Zbm3Eff/yJ2rW7z/kDciFPOBYD4FgUEeFwy5cv1rhx79iMe/zxJ/XGGyOtxnhKT0F/o1Ghvl7/JVkKiAAKGVs9lWzNdhh4TxXT/ZAtMxSxKEL3jLH+mtGt+iqpfIMsr0cbCQCALfa2XVq3bqNuv728k0eTfzzlWAyA4/Cph0OMHz9Gy5Ytshn3+edz1axZC7u3S09BAHB/9vRUupaYYnUbw3rdp3Ix/9p8rZ9+2qqioWVpIwEAsFtKSrKaNKlvV+zevUfk4+NjO9ADcCwG4GYUEZErRqNRLVo0VGJios3YLVt2qmTJ4Fy9Dj0FAcD92dNTKdtshtRklVs22HT3mpXt799/VF5emS5Rpo0EAMCG8+fPqUsX25cfFytWTL/9ti/Pr5fXhcPyA8diAG5GERF2S0xM1IMPtlNMTLTN2GwHdGbYm0jpKQgA7s2enkoVS/ir7a3eOjb5JZvbSw0sraiHxkmSIv7Xxmy+oY0EAOBmBw/u1zPPPG4z7vnnX9Tgwf9z2OvaaulRkHEsBiAziogwMVfUizp/Tp07d7D53PBHn9CoN9+2+7Vymkg5GASA/JfbWRTWeib5X/pTPe/pa3MbCa1e0NXyd2X5GZdSAQBsWbJkod57b6zNuOXLv1P16mEOf317WnoU9IIcx2IAMlBEhKSsRb0ifx9WqS3TbT4nunV/Jd2e3jvkeFgZxcpLV+KSbR5YekIiBYDCJi+zKG7uqVT8j/UKOrjK5mvOXfWj6lStohRJfbmUCgBgB6PRqKFDB2vTpp9sxu7YsV9FixZ16njsaekR6mv9Ci4AKCgoItpp06ZNWr16tQ4fPqyoqCgFBgaqUqVK6tChgx577DEFBgbm9xBzPUMk2ctLT360SKcWT1K55ASrsf+Efyhjkey/64bIKP0ZFa+H5/wuyfqBJYkUANxLXk/++BkM6up3QhGL3rD6OjVqhGnhwhXy8/P774dGo/wlLqUCAFiUkpKsr76aq08+mWw1Ljz8EY0a9a6LRpXOnpYephl+AFDAUUS0IT4+XkOHDtXPP/+c5efR0dGKjo7W/v379c0332jKlClq0KBB/gxSOZ8hYjAYtHr1So0ZM9L0M28z200uXVWb1n2nckV8dSIuRS1nbLc4hsTUNNNtaweWJFIAcC+5OfmTlJSkOXO+0Oefz7C67SeeeEbDhr1pcwxcSgUAyCw2NkZTp36kVauWW40bN+59denS1UWjys5aSw97HgeAgoRvLCvS0tI0ZMgQbdu2TZJUunRpPfLII6pevbquXr2qtWvXat++fbp48aJeeuklLVq0SNWqVXP5OO2dIXLjxg3NmjVTc+Z8YXV71+t2Vly9h0z341OMUhHbCS7A1yfb65s7sCSRAoB7sffkT3T0v/roo/e1bt13VuM//HCqOnS435FDBAC4OXuuqjp9+pTGj39He/b8bnE7AUWLasrshWpUp7bZGeuuXiX55pYemdHbF4C7oVpjxbJly0wFxOrVq2v+/PkqXbq06fEnnnhC77//vubMmaOrV69q1KhRWrBggcvHaW2GyMYDxzV0+yxt3fijxec3bNpC60MfUFqJsmYfzyjqWUuA7WuU1q6zMdl+bm5WIYkUANyLtZM7vrEXNKr/U/rj0AGLMQ8/3F2vvDJMpUqVcsLoAADuztpVVRG//ap33hmhS5f+sfj8Z1/qr99Lt9Sm09clSV3XXFLHPw3Zrspy5CrJ9hYj/Y1GzQyvq3709gXgASgiWpCWlqbp0/9bXGTSpElZCogZhg4dqh07dujo0aPas2ePfv31V91zzz2uHGq2GSK+0X8p+Pdv5BfzlyRpq5nn9Oz5qAYPfk1BQSWV7OWlPjcltQyZi3qWEmD7GqU1uFUV9f5mX7bnmzvwJJECgHu5+eRPkQsRKrnrK/kkpR+s/WHmOQMGvKJnnnle/v7+LhwpAMDdZLuqymhQsZO/KmLRQt0zxvxzvL299c474/XQQ92U4u2t51dGaJONq7IcubhjTouRgUYjvX0BeASKiBbs3r1bUVHpB0tNmzZVnTp1zMb5+Pjoqaee0ltvvSVJWrduncuLiBmFOq+URN26/BWLca++Oky9ez+dtWG9clbUuzkBlgjw1c6zMer9zT7FJ6dl2a61WYUkUgBwHxl5os+cn/Xn1H5mY4oUKaIxYybo/vs7ycuLxbEAAPbJfFWVX9QJld74odm4ihUr6Z13xqtRo7ssPv9mmdsr2Yr7N9mg+CTblzjnthhJb18AnoAiogVbt/43f69169ZWYzM/nvl5rpIxQ2Tj/j+z/NzgV1SVuw/SsreeVxEb28hJUe/mBNi++i1qWblUjmcVkkgBwH0EGo0aUMtfgzP9rGq1Gnpn9LuqX79Bfg0LAODmMl9V5X/lVJbHEsvV0ZdTPtA9Nava9XxLj4cG+tmMi7wSp15f7ZVkfVZhbhYbAwBPQRHRgsjISNPtevXqWY0tU6aMypUrp4sXL+rKlSuKjo52ad8n00xCSZuM4+SVkqjUkPKm5FfEzuJcbot6zCoEgMKhdYuWWrx4lYKCgnTbbbfn93AAAB4gc/uj+JrtlVy6qlJL3i6jf1FJUli1KnY/39rjOVkk0tqsQnuLlgDgiSgiWnD69GnT7fLly9uML1+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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-10-24_44_1.png" } }, "output_type": "display_data" } ], "source": [ "x = 10*np.random.random(20)\n", "y_pred = 3*x\n", "ex_df = pd.DataFrame(data = x,columns = ['x'])\n", "ex_df['y_pred'] = y_pred\n", "n_levels = range(1,18,2)\n", "noise = (np.random.random(20)-.5)*2\n", "for n in n_levels:\n", " y_true = y_pred + n* noise\n", " ex_df['r2 = '+ str(np.round(r2_score(y_pred,y_true),3))] = y_true\n", "\n", "f_x_list = [2*x,3.5*x,.5*x**2, .03*x**3, 10*np.sin(x)+x*3,3*np.log(x**2)]\n", "for fx in f_x_list:\n", " y_true = fx + noise\n", " ex_df['r2 = '+ str(np.round(r2_score(y_pred,y_true),3))] = y_true \n", "\n", "xy_df = ex_df.melt(id_vars=['x','y_pred'],var_name='rscore',value_name='y')\n", "# sns.lmplot(x='x',y='y', data = xy_df,col='rscore',col_wrap=3,)\n", "g = sns.FacetGrid(data = xy_df,col='rscore',col_wrap=3,aspect=1.5,height=3)\n", "g.map(plt.plot, 'x','y_pred',color='k')\n", "g.map(sns.scatterplot, \"x\", \"y\",)" ] }, { "cell_type": "markdown", "id": "270c2055", "metadata": {}, "source": [ "By default, the regression estimator uses the R2 score." ] }, { "cell_type": "code", "execution_count": 29, "id": "58aed85e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5244799162296381" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr.score(tips_X_test,tips_y_test)" ] }, { "cell_type": "code", "execution_count": null, "id": "c698785d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "dd930d67", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "49ad6552", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "aa6ab7c3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "58bbfb29", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.14.1" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, "source_map": [ 12, 20, 31, 38, 42, 46, 50, 63, 68, 75, 79, 95, 105, 132, 136, 138, 142, 148, 151, 153, 158, 160, 167, 173, 175, 178, 180, 184, 186, 204, 208, 210, 217, 220, 232, 239, 246, 248, 251, 253, 256, 258, 276, 278, 297, 318, 322, 328, 332, 336, 340, 344 ] }, "nbformat": 4, "nbformat_minor": 5 }