Machine Learning with Tree-Based Models in Python__Classification and Regression Trees.py

# -*- coding: utf-8 -*-
"""
Created on Fri Aug  2 09:13:52 2019

@author: z
"""

#####Classification and Regression Trees
###train 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)



###Using entropy as a criterion
# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier

# Instantiate dt_entropy, set 'entropy' as the information criterion
dt_entropy = DecisionTreeClassifier( max_depth=8,criterion='entropy',  random_state=1)

# Fit dt_entropy to the training set
dt_entropy.fit(X_train, y_train)



###Entropy vs Gini index
# Import accuracy_score from sklearn.metrics
from sklearn.metrics import accuracy_score

# Use dt_entropy to predict test set labels
y_pred= dt_entropy.predict(X_test)

# Evaluate accuracy_entropy
accuracy_entropy = accuracy_score(y_test, y_pred)

# Print accuracy_entropy
print('Accuracy achieved by using entropy: ', accuracy_entropy)

# Print accuracy_gini
print('Accuracy achieved by using the gini index: ', accuracy_gini)



###Train your first regression tree
# Import DecisionTreeRegressor from sklearn.tree
from sklearn.tree import DecisionTreeRegressor

# Instantiate dt
dt = DecisionTreeRegressor(max_depth=8,
             min_samples_leaf=0.13,
            random_state=3)

# Fit dt to the training set
dt.fit(X_train, y_train)



###Evaluate the regression tree
# Import mean_squared_error from sklearn.metrics as MSE
from sklearn.metrics import mean_squared_error as MSE

# Compute y_pred
y_pred = dt.predict(X_test)

# Compute mse_dt
mse_dt = MSE(y_test, y_pred)

# Compute rmse_dt
rmse_dt = mse_dt**(1/2)

# Print rmse_dt
print("Test set RMSE of dt: {:.2f}".format(rmse_dt))



###Linear regression vs regression tree
# Predict test set labels 
y_pred_lr = lr.predict(X_test)

# Compute mse_lr
mse_lr = MSE(y_pred_lr, y_test)

# Compute rmse_lr
rmse_lr = mse_lr**(1/2)

# Print rmse_lr
print('Linear Regression test set RMSE: {:.2f}'.format(rmse_lr))

# Print rmse_dt
print('Regression Tree test set RMSE: {:.2f}'.format(rmse_dt))