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Course Notes
Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! For courses that use data, the datasets will be available in the datasets
folder.
# Import any packages you want to use here
Take Notes
Add notes here about the concepts you've learned and code cells with code you want to keep.
Add your notes here
# Add your code snippets here
Classification and Regression Trees
Decision tree for classification
Train your first classification tree
# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier
# Instantiate a DecisionTreeClassifier 'dt' with a maximum depth of 6
dt = DecisionTreeClassifier(max_depth=6, random_state=SEED)
# Fit dt to the training set
dt.fit(X_train, y_train)
# Predict test set labels
y_pred = dt.predict(X_test)
print(y_pred[0:5])
Evaluate the classification tree
# Import accuracy_score
from sklearn.metrics import accuracy_score
# Predict test set labels
y_pred = dt.predict(X_test)
# Compute test set accuracy
acc = accuracy_score(y_test, y_pred)
print("Test set accuracy: {:.2f}".format(acc))
Logistic regression vs classification tree
# Import LogisticRegression from sklearn.linear_model
from sklearn.linear_model import LogisticRegression
# Instatiate logreg
logreg = LogisticRegression(random_state=1)
# Fit logreg to the training set
logreg.fit(X_train, y_train)
# Define a list called clfs containing the two classifiers logreg and dt
clfs = [logreg, dt]
# Review the decision regions of the two classifiers
plot_labeled_decision_regions(X_test, y_test, clfs)
Classification tree Learning
Using entropy as a criterion