Machine Learning : Random Forest Classification (Part 16)

It's also referred to as Ensemble learning (using multiple ml algorithm to predict something)

Steps:

It's basically a cluster of a lot of decision tree classification. Takes what majority of the decision tree predicts.

Let's code it down:

Problem statement: We are launching a new SUV in the market and we want to know which age people will buy it. Here is a list of people with their age and salary. Also, we have previous data of either they did buy any SUV before or not.

Let's import libraries , dataset and split

Feature Scaling

Training the Random Forest Classification model on the Training set

from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) #we are taking 10 trees

classifier.fit(X_train, y_train)

Predicting a new result

print(classifier.predict(sc.transform([[30,87000]])))

Predicting the Test set results

y_pred = classifier.predict(X_test)

print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

Making the Confusion Matrix

Visualizing the Training set results

Visualizing the Test set results

The code and dataset

Done!