In today’s blog, I have gone through the random forest technique for the analysis of datasets. The Random Forest ensemble learning algorithm is widely used in data analysis, machine learning, and statistics. It is especially effective for classification and regression tasks. An ensemble learning method combines multiple models’ predictions to improve overall accuracy and robustness.

The Random forest works in a different approaches like decision tree, ensemble learning, and voting and aggregation. The key advantages of random forest are:

Reduced Overfitting: Overfitting is reduced as a result of the randomness introduced during the tree-building process, making the model more robust and generalizable.

Random Forest gives a measure of the importance of each feature in making accurate predictions. This can help with feature selection.

High Accuracy: When compared to a single decision tree, Random Forest frequently produces high-accuracy models.

Handle Missing Values: Random Forest is robust to outliers and can handle missing values in the dataset.

I will implement this technique in my work so that it can be understandable the structure of decision tree and provide the insights of the data for the police shooting.