Beta
Supervised Learning with scikit-learn
Run the hidden code cell below to import the data used in this course.
# Importing pandas
import pandas as pd
# Importing the course datasets
diabetes = pd.read_csv('datasets/diabetes_clean.csv')
music = pd.read_csv('datasets/music_clean.csv')
advertising = pd.read_csv('datasets/advertising_and_sales_clean.csv')
telecom = pd.read_csv("datasets/telecom_churn_clean.csv")Take Notes
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# Add your code snippets hereSupervised learning
- classification : variables discrètes (s'il n'y en a que deux, on dit que la classification est binaire)
- regression : variables continues
Conventions de nommage
- feature (caractéristique) aussi appelée predictor variable ou independent variable
- target variable (vairable cible) encore appelée dependent variable ou response variable
- Requirements
- no missing values
- data in numeric format
- data stored in pandas DataFrame or NumPy array
- Perform Exploratory Data Analysis (EDA)
Classifying labels of unseen data
- Build a model
- Model learns from the labeled data (training data) we pass to it
- Pass unlabeled data to he model as input
- Model predicts the label of the unseen data
Scikit exige que les feature soient dans un tableau où chaque colonne est une caractéristique et chaque ligne une observation différente. De même, la vairable dépendante doit être dans un tableau à une dimension. Le nombre d'observations doit être, en outre, le même dans les deux tableaux
# Import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
# Create arrays for the features and the target variable
y = churn_df["churn"].values
X = churn_df[["account_length", "customer_service_calls"]].values
# Create a KNN classifier with 6 neighbors
knn = KNeighborsClassifier(n_neighbors=6)
# Fit the classifier to the data
knn.fit(X, y)
X_new = np.array([[30.0, 17.5],
[107.0, 24.1],
[213.0, 10.9]])
# Predict the labels for the X_new
y_pred = knn.predict(X_new)
# Print the predictions for X_new
print("Predictions: {}".format(y_pred)) Hidden output
# Import the module
from sklearn.model_selection import train_test_split
X = churn_df.drop("churn", axis=1).values
y = churn_df["churn"].values
# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
knn = KNeighborsClassifier(n_neighbors=5)
# Fit the classifier to the training data
knn.fit(X_train, y_train)
# Print the accuracy
print(knn.score(X_test, y_test))
# Create neighbors
neighbors = np.arange(1, 13)
train_accuracies = {}
test_accuracies = {}
for neighbor in neighbors:
# Set up a KNN Classifier
knn = KNeighborsClassifier(n_neighbors=neighbor)
# Fit the model
knn.fit(X_train, y_train)
# Compute accuracy
train_accuracies[neighbor] = knn.score(X_train, y_train)
test_accuracies[neighbor] = knn.score(X_test, y_test)
print(neighbors, '\n', train_accuracies, '\n', test_accuracies)
# Add a title
plt.title("KNN: Varying Number of Neighbors")
# Plot training accuracies
plt.plot(neighbors, train_accuracies.values(), label="Training Accuracy")
# Plot test accuracies
plt.plot(neighbors, test_accuracies.values(), label="Testing Accuracy")
plt.legend()
plt.xlabel("Number of Neighbors")
plt.ylabel("Accuracy")
# Display the plot
plt.show()import numpy as np
# Create X from the radio column's values
X = sales_df["radio"].values
# Create y from the sales column's values
y = sales_df["sales"].values
# Reshape X (pour en faire un tableau 2D)
X = X.reshape(-1,1)
# Check the shape of the features and targets
print(X.shape, y.shape)
# Import LinearRegression
from sklearn.linear_model import LinearRegression
# Create the model
reg = LinearRegression()
# Fit the model to the data
reg.fit(X,y)
# Make predictions
predictions = reg.predict(X)
print(predictions[:5])
# Import matplotlib.pyplot
import matplotlib.pyplot as plt
# Create scatter plot
plt.scatter(X, y, color="blue")
# Create line plot
plt.plot(X, predictions, color="red")
plt.xlabel("Radio Expenditure ($)")
plt.ylabel("Sales ($)")
# Display the plot
plt.show()
# Create X and y arrays
X = sales_df.drop("sales", axis=1).values
y = sales_df["sales"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Instantiate the model
reg = LinearRegression()
# Fit the model to the data
reg.fit(X_train, y_train)
# Make predictions
y_pred = reg.predict(X_test)
print("Predictions: {}, Actual Values: {}".format(y_pred[:2], y_test[:2]))
# Import mean_squared_error
from sklearn.metrics import mean_squared_error
# Compute R-squared
r_squared = reg.score(X_test, y_test)
# Compute RMSE
rmse = mean_squared_error(y_test, y_pred, squared=False)
# Print the metrics
print("R^2: {}".format(r_squared))
print("RMSE: {}".format(rmse))# Import the necessary modules
from sklearn.model_selection import cross_val_score, KFold
# Create a KFold object
kf = KFold(n_splits=6, shuffle=True, random_state=5)
reg = LinearRegression()
# Compute 6-fold cross-validation scores
cv_scores = cross_val_score(reg, X, y, cv=kf)
# Print scores
print(cv_scores)
# Import Ridge
from sklearn.linear_model import Ridge
alphas = [0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0]
ridge_scores = []
for alpha in alphas:
# Create a Ridge regression model
ridge = Ridge(alpha=alpha)
# Fit the data
ridge.fit(X_train, y_train)
# Obtain R-squared
score = ridge.score(X_test, y_test)
ridge_scores.append(score)
print(ridge_scores)
# Import Lasso
from sklearn.linear_model import Lasso
# Instantiate a lasso regression model
lasso = Lasso(alpha=0.3)
# Fit the model to the data
lasso.fit(X, y)
# Compute and print the coefficients
lasso_coef = lasso.fit(X,y).coef_
print(lasso_coef)
plt.bar(sales_columns, lasso_coef)
plt.xticks(rotation=45)
plt.show()# Import confusion matrix
from sklearn.metrics import classification_report, confusion_matrix
knn = KNeighborsClassifier(n_neighbors=6)
# Fit the model to the training data
knn.fit(X_train, y_train)
# Predict the labels of the test data: y_pred
y_pred = knn.predict(X_test)
# Generate the confusion matrix and classification report
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
# Import LogisticRegression
from sklearn.linear_model import LogisticRegression
# Instantiate the model
logreg = LogisticRegression()
# Fit the model
logreg.fit(X_train, y_train)
# Predict probabilities
y_pred_probs = logreg.predict_proba(X_test)[:, 1]
print(y_pred_probs[:10])
# Import roc_curve
from sklearn.metrics import roc_curve
# Generate ROC curve values: fpr, tpr, thresholds
fpr, tpr, thresholds = roc_curve(y_test, y_pred_probs)
plt.plot([0, 1], [0, 1], 'k--')
# Plot tpr against fpr
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve for Diabetes Prediction')
plt.show()
# Import roc_auc_score
from sklearn.metrics import roc_auc_score
# Calculate roc_auc_score
print(roc_auc_score(y_test, y_pred_probs))
# Calculate the confusion matrix
print(confusion_matrix(y_test, y_pred))
# Calculate the classification report
print(classification_report(y_test, y_pred))
# Import GridSearchCV
from sklearn.model_selection import GridSearchCV
# Set up the parameter grid
param_grid = {"alpha": np.linspace(0.00001, 1, 20)}
# Instantiate lasso_cv
lasso_cv = GridSearchCV(lasso, param_grid, cv=kf)
# Fit to the training data
lasso_cv.fit(X_train, y_train)
print("Tuned lasso paramaters: {}".format(lasso_cv.best_params_))
print("Tuned lasso score: {}".format(lasso_cv.best_score_))
# Create the parameter space
params = {"penalty": ["l1", "l2"],
"tol": np.linspace(0.0001, 1.0, 50),
"C": np.linspace(0.1, 1.0, 50),
"class_weight": ["balanced", {0:0.8, 1:0.2}]}
# Instantiate the RandomizedSearchCV object
logreg_cv = RandomizedSearchCV(logreg, params, cv=kf)
# Fit the data to the model
logreg_cv.fit(X_train, y_train)
# Print the tuned parameters and score
print("Tuned Logistic Regression Parameters: {}".format(logreg_cv.best_params_))
print("Tuned Logistic Regression Best Accuracy Score: {}".format(logreg_cv.best_score_))