#decisiontrees

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.

📊 If you want to understand how machines make decisions and why ensemble models outperform single algorithms, this video is for you!

📌 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 predictive models that allow for a data driven exploration of nonlinear relationships and interactions among many explanatory variables in predicting a response or target variable. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.

How to build a decision tree with python

1. Split your dataset into train and test sets

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

2. Import skelarn DecisionTreeClassifier then fit on your train dataset

classifier=DecisionTreeClassifier()

classifier=classifier.fit(pred_train,tar_train)

3. Finally you can do predictions on your test dataset

predictions=classifier.predict(pred_test) 

4. You can also visualize your  tree with Graphviz and pydotplus

tree.export_graphviz(classifier, out_file=out)

graph=pydotplus.graph_from_dot_data(out.getvalue())

Morning Reading Session(6am-8am): Decision Trees/SVM/k-nearest neighbor

#Iran_Goodfellow #Yoshua_Bengio #Aaeon_Courville