Why is feature selection required in machine learning.
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As different type of machine learning models are available, often model selection methods are used. Why it is important. Can we not select one model based on past experience.
what is no free lunch theorem in Machine Learning.
In machine learning, often data is divided into training and validation set. What is the importance of validation set.
What is generalization error in machine error.
Loosely how can supervised learning be converted into unsupervised learning and vice-versa?
What is the removal of the recursive feature in data pre-processing?
What is the forward selection of data pre-processing?
What is regularization in machine learning and why it is important?
What is confusion matrix and how it is used in machine learning?