Introduction Affinity Propagation was first published in 2007 by Brendan Frey and Delbert Dueck and It is only getting more and more popular due to its simplicity, general applicability, and performance. Affinity Propagation is an unsupervised machine learning algorithm unlike clustering algorithms such as K means clustering. The main drawbacks of K-Means and similar algorithms…
What is Model Fitting? Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. The generalization of a model to new data is ultimately what allows us to use machine learning algorithms every day to make predictions and classify data. The definition of a…
In machine learning, there are many ways (an infinite number, really) of solving any one problem. Thus it is important to have an objective criterion for assessing the accuracy of candidate approaches and for selecting the right model for a dataset at hand. In this post, we’ll discuss the concepts of underfitting and overfitting, and…
Recently, I was implementing a Random Forest regressor when I faced the classical question: Should I implement data normalization? Before going into the depth of the topic, we will try to understand what normalization is. Normalization The goal of normalization is to change the values of numeric columns in the dataset to a common…
