#decisiontrees

[shivam1605]

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

🎓 Why Learn with Imarticus Learning?

✔️ Learn from experienced industry professionals ✔️ Flexible, structured learning programs ✔️ Mock tests, mentorship, and comprehensive study resources ✔️ Career-focused training that delivers real outcomes

🔥 Break into AI & ML — Build Smarter Models, Secure Top Jobs!

The Postgraduate Program in Data Science and Analytics (PGA) is a 6-month program designed for graduates and early professionals. It offers 100% job assurance, training across 10+ tools like Python, Power BI, and Tableau, 25+ hands-on projects, and 300+ learning hours. With salary hikes of up to 52% and a highest package of ₹22.5 LPA, this program prepares you for high-impact careers in data science and analytics.

Start building intelligent models—and a future-ready career—with Imarticus Learning.

[sunshinedigitalservices]

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[gtdk]

Running a classification tree with python

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())

Above is the obtained decision tree.

[incegna]

Decision tress is a tree like graph with nodes.It is one of the simplest and most useful machine learning structures.These are a type of supervised learning where the data will split continuously according to the certain parameters.The trees can be explained in to two entities as decision nodes and leaves.These trees builds under classification or regression models in a tree structure which breaks the data into smaller and tiny subsets.This is a commonly used tool in data mining #decisiontrees,#graph,#nodes,#supervised,#regression,#split,#small,#machinelearning Check our info : www.incegna.com Live session reg : http://www.incegna.com/contact-us Like us on Facebook : http://www.facebook.com/INCEGNA/ https://www.instagram.com/p/B0aGONSnOB-/?igshid=1251m8izfh98e

[whitechnoleg]

edvancer.in/logistic-regression-vs-decision-trees-vs-svm-part1 // #machinelearning #logisticregression #decisiontrees #svm

[haohanwang]

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

#Iran_Goodfellow #Yoshua_Bengio #Aaeon_Courville

#decisiontrees #svm #k-nearest_neighbor #mxmnml