Python End-to-end projects | Video 5: Switching gears from Decision Trees to Random Forest Algorithm

How can we improve the accuracy of your model with hyper parameter tuning and pivoting to Random Forest Classifier. All under 10 mins.

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In this video, the presenter discusses enhancing model accuracy by switching from Decision Tree classifiers to Random Forest algorithms. Here's a quick breakdown:

Recap of Decision Trees [0:12]: The video starts with a quick recap of developing decision tree classifiers using Python and a Kaggle dataset.
Random Forest Classifier [0:41]: The presenter introduces the Random Forest classifier as a way to potentially enhance model accuracy.
Hyperparameter Tuning [0:55]: The video highlights the relevance of hyperparameter tuning in improving model accuracy.
Genie and Entropy [2:17]: The presenter introduces Genie and entropy as hyperparameters, explaining that they are both measurements of impurity [5:50]. Genie varies from 0 to 0.5, while entropy varies from 0 to 1 [5:58].
Model Evaluation [6:47]: The presenter compares the model evaluation metrics of Decision Trees and Random Forest, noting that accuracy improved when using Random Forest.
Key Takeaway [9:45]: By switching from Decision Trees to Random Forest, a 20% higher recall was achieved.

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