Logistic Regression

Explore logistic regression to predict binary outcomes by understanding its mathematical foundation, training models with Scikit-Learn, and evaluating them using classification metrics. Practice with datasets like breast cancer and generated examples to visualize decision boundaries and assess model performance.

We'll cover the following...

- What is logistic regression?
- Modeling on breast cancer dataset
- Area Under the Curve
- Modeling on the generated dataset and draw the decision boundary

What is logistic regression?

Logistic regression is a Machine Learning classification algorithm that is used to predict the probability of certain classes based on some dependent variables. In short, the logistic regression model computes a sum of the input feature (in most cases, there is a bias term), and calculates the logistic of the result.

The output of a logistic regression is always between (0, 1), which is suitable for a binary classification task. The higher the value, the higher the probability that the current sample is classified as class=1, and vice versa.

h_{\theta}(x) = \frac{1}{1+e^{-\theta x}}

As the formula above shows, $\theta$ is the parameter we want to learn or train or optimize and $x$ ...

1.Preliminaries

2.Working with Datasets

3.Feature Engineering

4.General Concepts

5.Linear Regression

6.Logistic Regression

7.Support Vector Machine

8.Tree Model and Ensemble Method

9.Unsupervised Learning

10.Deep Learning

11.Others

12.What's Next

Logistic Regression

What is logistic regression?