[Python] Random Forest Regression.ipynb

Kernel: Python 3

Random Forest Regression

Data Processing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
%matplotlib inline
plt.rcParams['figure.figsize'] = [14, 8]

# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

Fitting the Random Forest Regression Model to the dataset

regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)
regressor.fit(X, y)

RandomForestRegressor(bootstrap=True, criterion='mse', 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=300, n_jobs=1,
           oob_score=False, random_state=0, verbose=0, warm_start=False)

Visualising the Random Forest Regression results (for higher resolution and smoother curve)

X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Random Forest Regression Model)')
plt.xlabel('Level')
plt.ylabel('Salary')
plt.show()

Predicting a new result

y_pred = regressor.predict(6.5)
y_pred

array([ 160333.33333333])

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Here the predicted value is almost equal to 160K which is the Salary value prposed by the employee. Also the model seems to be much better then the Polynomial Regression Model.

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