iframe-proxy | Sunbelt Computer Software

History

66 lines (56 loc) · 2.27 KB

Raw

"""Implementation of GradientBoostingRegressor in sklearn using the

boston dataset which is very popular for regression problem to

predict house price.

"""

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.datasets import load_boston

from sklearn.ensemble import GradientBoostingRegressor

from sklearn.metrics import mean_squared_error, r2_score

from sklearn.model_selection import train_test_split

def main():

# loading the dataset from the sklearn

df = load_boston()

print(df.keys())

# now let construct a data frame

df_boston = pd.DataFrame(df.data, columns=df.feature_names)

# let add the target to the dataframe

df_boston["Price"] = df.target

# print the first five rows using the head function

print(df_boston.head())

# Summary statistics

print(df_boston.describe().T)

# Feature selection

X = df_boston.iloc[:, :-1]

y = df_boston.iloc[:, -1] # target variable

# split the data with 75% train and 25% test sets.

X_train, X_test, y_train, y_test = train_test_split(

X, y, random_state=0, test_size=0.25

)

model = GradientBoostingRegressor(

n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01

)

# training the model

model.fit(X_train, y_train)

# to see how good the model fit the data

training_score = model.score(X_train, y_train).round(3)

test_score = model.score(X_test, y_test).round(3)

print("Training score of GradientBoosting is :", training_score)

print("The test score of GradientBoosting is :", test_score)

# Let us evaluation the model by finding the errors

y_pred = model.predict(X_test)

# The mean squared error

print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))

# Explained variance score: 1 is perfect prediction

print("Test Variance score: %.2f" % r2_score(y_test, y_pred))

# So let's run the model against the test data

fig, ax = plt.subplots()

ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))

ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)

ax.set_xlabel("Actual")

ax.set_ylabel("Predicted")

ax.set_title("Truth vs Predicted")

# this show function will display the plotting

plt.show()

if __name__ == "__main__":

main()

Sunbelt Computer Software

PL/B Language Development and Support

Navigation Menu

Sunbelt Computer Software

PL/B Language Development and Support

FilesExpand file tree

gradient_boosting_regressor.py

Latest commit

History

gradient_boosting_regressor.py

File metadata and controls