Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a ...

Logistic Regression is performed with a few lines of code using the SciKit-Learn library. from sklearn.linear_model import LogisticRegression model_2 = LogisticRegression(penalty='none') model_2.fit(X ...

In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code behind the linear regression in python. Your Lane to ...

8.3. Regression diagnostics¶. Like R, Statsmodels exposes the residuals. That is, keeps an array containing the difference between the observed values Y and the values predicted by the linear model. A ...

This repository contains the materials for D-Lab’s Python Machine Learning workshop. In this workshop, we provide an introduction to machine learning in Python. First, we'll cover some machine ...

An alternative approach is to code the accuracy method so that the second parameter is interpreted as a percentage. For example, a value of 0.10 means a predicted count is correct if it is between ...