Feature selection using Decision Tree

  One of the key differentiators in any data science problem is the quality of feature selection and importance. When we have a lot of data available to be used by our model, the task of feature selection becomes inevitable due to computational constraints and the elimination of noisy variables for better prediction. Also,...

How to avoid overfitting while training?

Overfitting happens mostly because the model becomes too complex. Such a model will give poor accuracies, as it memorizes the noise in the training data. A model is usually fit by achieving the highest accuracy on the training data set. However, its efficiency is judged by its its performance on test data. Overfitting occurs...