What is multicollinearity? Data Science by Sunny Srinidhi - August 8, 2018January 30, 20200 Multicollinearity is a term we often come across when we’re working with multiple regression models. Even though we have talked about it in our previous posts, do we know what it actually means? Today, we’ll try to understand that. In most real life problems, we usually have multiple features to work with. Read more... “What is multicollinearity?”
Overfitting and Underfitting models in Machine Learning Data Science by Sunny Srinidhi - August 2, 20180 In most of our posts about machine learning, we've talked about overfitting and underfitting. But most of us don't yet know what those two terms mean. What does it acutally mean when a model is overfit, or underfit? Why are they considered not good? And how do they affect the accuracy of our model's predictions? These are some of the basic, but important questions we need to ask and get answers to. So let's discuss these two today. The datasets we use for training and testing our models play a huge role in the efficiency of our models. Its equally important to understand the data we're working with. The quantity and the quality of the data also matter, obviously. When the data
Different types of Validations in Machine Learning (Cross Validation) Data Science by Sunny Srinidhi - August 1, 20180 Now that we know what is feature selection and how to do it, let's move our focus to validating the efficiency of our model. This is known as validation or cross validation, depending on what kind of validation method you're using. But before that, let's try to understand why we need to validate our models. Validation, or Evaluation of Residuals Once you are done with fitting your model to you training data, and you've also tested it with your test data, you can't just assume that its going to work well on data that it has not seen before. In other words, you can't be sure that the model will have the desired accuracy and variance in your production environment. You need