Evaluation Metrics for Binary Classification
So far, we have discussed the task of classification analysis along with a few version of logistic regression. We have also talked a bit about evaluating a classification task with…
Logistic Regression
With the previous introduction to classification analysis, we can now discuss more about classification models. Since we have had some exposure to logistic regression last time, let us continue with…
Classification Analysis
At this point, we have spent several posts talking about regression analysis. While it is surely not everything about regression, let us change the air a little bit and discuss…
Regularized linear models
Previously, we have learned about model tuning as well as discussed more about Ridge regression which is one of the regularized linear models. As it turns out, Ridge regression is…
Model Tuning
In the last post, we talked about model regularization and demonstrated the concept with a model called Ridge regression. However, the Ridge model we used back then was actually just…
Model Regularization
Previously, we have discussed the overfitting problem where a model overlearns the training data and fails to generalize. There are a lot of potential issues here. First, overfitting is obviously…
The Overfitting Problem
So, I am here and let me fulfill my promise in the last post. Do you remember? We performed quadratic regression on the auto-mpg data and get a much better…
Polynomial Regression
The basic linear regression model is fairly limited in that it requires the features and the target to have a linear correlation to perform well. In practice though, we commonly…
Cross-Validation
Remember how I said we had not learned to properly perform model evaluation a bit ago? Well, let us fix that today then! Recall, the point of predictive analysis is…
Linear Regression Model
After the first step of looking at the most basic regression problem, hopefully you have had a good understand about this type of analysis and its approach. Therefore, it is…