Implementation of Random Forest Classification in Python

Implementation of Random Forest Classification in Python – Machine Learning

In this tutorial, we will understand the Implementation of Random Forest Classification in Python – Machine Learning.

Importing the Necessary libraries

To begin the implementation first we will import the necessary libraries like NumPy for numerical computation and pandas for reading the dataset.

import numpy as np
import pandas as pd

Importing the dataset

Next, we import or read the dataset. Click here to download the breast cancer dataset used in this implementation. After reading the dataset, divide the dataset into concepts and targets. Store the concepts into X and targets into y.

dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

Splitting the dataset into the Training set and Test set

Once the dataset is read into the memory, next, divide the dataset into two parts, training and testing using the train_test_split function from sklearn. The test_size and random_state attributes are set to 0.25 and 0 respectively. You can change these attributes as per your requirements.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

Feature Scaling

Feature scaling is the process of converting the data into a min-max range. In this case, the standard scalar method is used.

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

Training the Random Forest Classification model on the Training set

Once the dataset is scaled, next, the Naive Bayes classifier algorithm is used to create a model. The GaussianNB function is imported from sklearn.naive_bayes library. The hyperparameters such as kernel, and random_state to linear, and 0 respectively. The remaining hyperparameters of the support vector machine algorithm are set to default values.

from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)

Random Forest classifier model

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',
                       max_depth=None, max_features='auto', max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=10,
                       n_jobs=None, oob_score=False, random_state=0, verbose=0,
                       warm_start=False)

Display the results (confusion matrix and accuracy)

Here evaluation metrics such as confusion matrix and accuracy are used to evaluate the performance of the model built using a decision tree classifier.

from sklearn.metrics import confusion_matrix, accuracy_score
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)

Output

Confusion Matrix of Random Forest Classifier

Implementation of Random Forest Classification in Python

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