Difference between revisions of "Python-for-Machine-Learning/C3/Random-Forest/English"

Welcome

Learning Objectives

• Ensemble Learning and
• Random Forest

• Ubuntu Linux operating system 24.04
• Jupyter Notebook IDE

Prerequisite

• The learner must have basic knowledge of Python.
• For pre-requisite Python tutorials, please visit this website.

Code files

• The files used in this tutorial are provided in the Code files link.
• Please download and extract the files.
• Make a copy and then use them while practicing.

Ensemble Learning

• Ensemble learning combines multiple models to improve performance.
• It reduces errors by averaging predictions from different models.
• One popular ensemble method is Random Forest, which uses decision trees.

Random Forest

• Random Forest builds multiple trees and takes the majority vote.
• This improves accuracy and reduces overfitting compared to a single tree.
• It is widely used for classification and regression tasks.

RandomForest.ipynb is the python notebook file for the demonstration of Random Forest

Type conda activate ml

Press Enter

Activate the machine learning environment by typing

conda space activate space ml

Press Enter.

Type cd Downloads Press Enter Type jupyter notebook Press Enter

Please navigate to the respective folder of your code file location.

Then type, jupyter space notebook and press Enter to open Jupyter Notebook.

Click on RandomForest.ipynb file

Click the RandomForest dot ipynb file to open it.

Note that each cell will have the output displayed in this file.

import pandas as pd import numpy as np from sklearn.metrics import accuracy_score

Please remember to Execute each cell by pressing Shift and Enter to get output.

california = fetch_california_housing()  

housing = pd.DataFrame(california.data, columns=california.feature_names)

housing['MedHouseVal'] = california.target

It has housing data from California districts.

We analyze the dataset and predict the MedHouseValue.

Now, we load the California Housing dataset into a Pandas DataFrame.

We add the target column, MedHouseVal, which represents median house value.

housing.head()

housing.shape

housing.info()

housing_sorted = housing.sort_values(by="HouseAge")

The shaded region represents price variability in different age groups.

skewed_features = ['AveRooms', 'AveBedrms', 'Population', 'AveOccup']

Next, we apply log transformation to reduce skewness in these features.

scaler = StandardScaler()

This ensures all features have a similar range for better modeling.

x = housing.drop(columns=["MedHouseVal"])

x.head()

After that, we display the first few rows to check the data.

y = housing.MedHouseVal y.head()

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)

rf =

Then, we train the model using the training data.

y_train_pred = rf.predict(x_train)

training_mse =

We also compute the adjusted R squared score for better evaluation.

The Training Adjusted R squared Score is 0.972.A high Adjusted R squared score suggests the model explains most variance.

y_pred = rf.predict(x_test)

print("Random Forest - Actual vs Predicted:")

test_mse = mean_squared_error(y_test,y_pred)

Then, we compute the Adjusted R squared score for the test set.

The Test Adjusted R squared Score is 0.804.

A higher Adjusted R Squared score indicates a good fit.

residuals = y_test - y_pred plt.show()

The red dashed line marks zero residuals for reference.

Most residuals are near zero, meaning the prediction error is low.

feature_importances = rf.feature_importances_

plt.show()

Feature importance shows how much each feature impacts predictions.

Higher value mean a feature is more important for the model.

Other features contribute less but still affect model performance.

The model showed high accuracy, indicating strong performance.

Summary

In this tutorial, we have learnt about

• Ensemble Learning and
• Random Forest

Assignment

• Replace the test_size parameter as shown here.
• Observe the change in MSE and Adjusted R squared score

Assignment Solution

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