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Commit 9a17b99

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Chapters 2 and 3 updates
1 parent e8e478e commit 9a17b99

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4 files changed

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‎Lesson02/.ipynb_checkpoints/Chapter_2_Case_Study-checkpoint.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Linear regression on synthetic data"
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"# Synthetic Data"
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"type(X)"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data for a Linear Regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"plt.scatter(X,y,s=1)"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Linear regression in Scikit-Learn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Calculating the True Positive Rate, False Positive Rate, and Confusion Matrix"
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"# Exercise X: Calculating the True and False Positive and Negative Rates and Confusion Matrix in Python"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Discovering Predicted Probabilities and the Creating Receiver Operating Characteristic (ROC) Curve"
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"# Exercise X: Obtaining Predicted Probabilities from a Trained Logistic Regression Model"
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]
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"plt.ylabel('Number of samples')"
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# The Receiver Operating Characteristic (ROC) curve"
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]
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},
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‎Lesson02/Chapter_2_Case_Study.ipynb

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@@ -326,7 +326,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Linear regression on synthetic data"
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"# Synthetic Data"
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]
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},
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{
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"type(X)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data for a Linear Regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"plt.scatter(X,y,s=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Linear regression in Scikit-Learn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Calculating the True Positive Rate, False Positive Rate, and Confusion Matrix"
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"# Exercise X: Calculating the True and False Positive and Negative Rates and Confusion Matrix in Python"
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]
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},
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{
@@ -1011,7 +1025,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise X: Discovering Predicted Probabilities and the Creating Receiver Operating Characteristic (ROC) Curve"
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"# Exercise X: Obtaining Predicted Probabilities from a Trained Logistic Regression Model"
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]
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},
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{
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"plt.ylabel('Number of samples')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# The Receiver Operating Characteristic (ROC) curve"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"attachments": {},
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"source": [

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