Then, we calculate the ratio of the weighted sum of the squares of the differences between each category's average and overall average to the sum of squares between each value and overall average. The range of eta is between 0 and 1. A value closer to 0 indicates all categories have similar values, and any single category doesn't have more influence on variable y. A value closer to 1 indicates one or more categories have different values than other categories and have more influence on variable y. Eta can be used in EDA and data processing to know which categorical features are more important in machine learning model building.

Logistic regression is regressing data to a line (i.e. This type of regression is a good choice when modeling binary variables, which happen frequently in real life (e.g. The logistic regression model is popular, in part, because it gives probabilities between 0 and 1. Let's say you were modeling a risk of credit default: values closer to 0 indicate a tiny risk, while values closer to 1 mean a very high risk. The following image shows an example of how one might tailor a logistic model for credit score based risk.

Logistic regression is regressing data to a line (i.e. This type of regression is a good choice when modeling binary variables, which happen frequently in real life (e.g. The logistic regression model is popular, in part, because it gives probabilities between 0 and 1. Let's say you were modeling a risk of credit default: values closer to 0 indicate a tiny risk, while values closer to 1 mean a very high risk. The following image shows an example of how one might tailor a logistic model for credit score based risk.

Prediction is an iteration task: you build your model, measure its performance and start again until you make progress. And progress is measured by metrics. Thus, metrics are the only way to understand if you are doing a good job or not. The choice of which metric to use depends on various factors, including the problem you are trying to solve; how you want to penalize errors and the model you are building. Let's discuss some of these metrics by clustering them by predictive model type.

Feature selection is one of the first and important steps while performing any machine learning task. A feature in case of a dataset simply means a column. When we get any dataset, not necessarily every column (feature) is going to have an impact on the output variable. If we add these irrelevant features in the model, it will just make the model worst (Garbage In Garbage Out). This gives rise to the need of doing feature selection.

We live in a world in which machine learning is at the core of the fourth industrial revolution. Linear regression is one of the simplest and most widely used machine learning techniques. There are a plethora of practical applications of linear regression. For example, obesity can be used to predict the chances of developing type 2 diabetes. Or, a student's GPA can be predicted based on the number of hours he/she spends studying.

When you think of data science and machine learning two programming languages are going to instantly pop into your mind: Python and R. These two languages are great and I love working with them, but coming from a .NET and C# background myself it would be nice to see some love for data science in the C# world. Well, now we have it with the announcement of ML.NET we can do just that! In this post, we will go over ML.NET by creating a very simple application for it to train against some data, how to evaluate the model, and then how to predict on new data. Since it is a .NET language F# developers can use it as well.