DECISION TREES AND RANDOM FOREST in Machine Learning

Decision Trees and Random Forest are among the most powerful and widely used algorithms in Machine Learning. In this video, we simplify the intuition and structure behind Decision Trees and explain how Random Forest combines multiple trees to deliver more accurate and stable predictions.

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📌 What You’ll Learn:

💡 Decision Trees Explained – Splits, nodes, and leaf structure made simple 🌲 Random Forest Demystified – How multiple trees work together for better results 📈 Overfitting & Pruning – Techniques to build smarter, more reliable models 🧠 Real-World Use Cases – Applications in customer churn, fraud detection, and more

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Decision Trees are simple yet powerful models for classification and prediction, but they can overfit easily. Random Forests overcome this by combining multiple trees to improve accuracy, reduce bias, and enhance generalization. Together, they form the foundation of smarter, more reliable machine learning systems Read More..

Random Forest Cloud Deployment for Beginners

Deploying ML on Cloud isn’t just about hosting your model—it’s about making it smarter, faster, and more reliable. One of the best ways to achieve this? Random Forest 🌲✨

In this Masterclass, discover how to implement a Random Forest Classifier to improve prediction accuracy for your sleep disorder detection project. Step by step, we’ll break down the logic, implementation, and evaluation of this powerful ensemble method.

📌 What You’ll Learn

1️⃣ Understanding Random Forest → Bagging, decision trees, and why Random Forest often outperforms single models.

2️⃣ Model Building in Python → Hands-on demo: train a Random Forest Classifier with Scikit-learn.

3️⃣ Performance Tuning → Fine-tune hyperparameters like n_estimators and max_depth for maximum accuracy.

4️⃣ Evaluation Metrics → Random Forest vs. Decision Tree: accuracy, confusion matrix, and F1-score.

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from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

Load the dataset

data = pd.read_csv("C:\Users\guy3404\OneDrive - MDLZ\Documents\Cross Functional Learning\AI COP\Coursera\machine_learning_data_analysis\Datasets\tree_addhealth.csv")

data.head()

Getting information aboubt the dataset

data.info()

Total size of data

len(data)

We observe some of the columns of the dataset contains null values . We need to drop them

Drop null values from dataset

data_clean = data.dropna()

data_clean.dtypes

data_clean.describe()

Length of dataset after dropping null values

len(data_clean)

Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

Build model on training data

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predict using random forest classifier on test data

predictions=classifier.predict(pred_test)

Print confusion matrix and accuracy score

sklearn.metrics.confusion_matrix(tar_test,predictions)

sklearn.metrics.accuracy_score(tar_test, predictions)

fit an Extra Trees model to the data

model = ExtraTreesClassifier() model.fit(pred_train,tar_train)

Get feature importances

feature_importances = model.feature_importances_

Create a Series with feature importances and corresponding feature names

feature_importance_series = pd.Series(feature_importances, index=pred_train.columns)

Sort features based on importance

sorted_feature_importance = feature_importance_series.sort_values(ascending=False)

Plot the feature importances

plt.figure(figsize=(10, 6)) sorted_feature_importance.plot(kind='barh') plt.title('Feature Importance') plt.xlabel('Importance Score') plt.show()

#Running different number of trees and see the effect of that on the accuracy of the prediction

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)): classifier=RandomForestClassifier(n_estimators=idx + 1) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)