FAQ on Machine Learning

ML algorithms can be primarily classified depending on the presence/absence of target variables. A. Supervised learning: [Target is present] The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data. The target variable is continuous: Linear Regression, polynomial Regression, quadratic Regression. The … Read more

OLS and Maximum likelihood are the methods used by the respective regression methods to approximate the unknown parameter (coefficient) value. In simple words, Ordinary least square(OLS) is a method used in linear regression which approximates the parameters resulting in minimum distance between actual and predicted values. Maximum Likelihood helps in choosing the the values of … Read more

Regularization becomes necessary when the model begins to ovefit / underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce cost term. This helps to reduce model complexity so that the model can become … Read more

For better predictions, categorical variable can be considered as a continuous variable only when the variable is ordinal in nature. Treating a categorical variable as continuous depends on the nature of the variable and its relationship with the target variable. Here’s how you could approach this question in a machine learning interview: Understanding the Nature … Read more

You should say, the choice of machine learning algorithm solely depends of the type of data. If you are given a data set which is exhibits linearity, then linear regression would be the best algorithm to use. If you given to work on images, audios, then neural network would help you to build a robust … Read more