Project - Regression Machine Learning Case Study

How do you work through a predictive modeling machine learning problem end-to-end? In this lesson you will work through a case study regression predictive modeling problem in Python including each step of the applied machine learning process. After completing this project, you will know:

• How to work through a regression predictive modeling problem end-to-end
• How to use data transforms to improve model performance 
• How to use algorithm tuning to improve model performance
• How to use ensemble methods and tuning of ensemble methods to improve model performance

Project - Predictive Modeling Project Template

Applied machine learning is an empirical skill. You cannot get better at it by reading books and articles. You have to practice. In this lesson you will discover the simple six-step machine learning project template that you can use to jump-start your project in Python. After completing this lesson you will know:

1. How to structure an end-to-end predictive modeling project.

2. How to best use the structured project template to ensure an accurate result for your dataset.

Optimal Threshold for Imbalanced Classification

Many machine learning algorithms are capable of predicting a probability or scoring of class membership and this must be interpreted before it can be mapped to a class label.

This is achieved by using a threshold, such as 0.5, where all values equal or greater than the threshold are mapped to one class and all other values are mapped to another class.

For those classification problems that have a severe class imbalance, the default threshold can result in poor performance. As such, a simple and straightforward approach to improving the performance of a classifier that predicts probabilities on an imbalanced classification problem is to tune the threshold used to map probabilities to class labels.

Understanding the ROC curve

Receiver Operating Characteristic (ROC) curve is a visual representation of how well your classification model works.

In this blog, we will explore how the ROC curve is constructed from scratch in three visual steps.

Bias Variance Tradeoff

4 Types of Classification Tasks in Machine Learning

Examples of classification problems include:

• Given an example, classify if it is spam or note
• Given a handwritten character, classify if as one of known characters
• Given recent user behavior, classify as churn or not

Classification requires a training dataset with many examples of inputs and outputs from which to learn.

14 Different Types of Learning in Machine Learning

There are 14 types of learning that you must be familiar with as a practitioners; they are:

Learning Problem

1.Supervised Learning 
2.Unsupervised Learning
3.Reinforcement Learning

Hybrid Learning Problem

4.Semi-Supervised Learning
5.Self-Supervised Learning
6.Multi-Instance Learning

Statistical Inference

7.Inductive Learning
8.Deductive Inference
9.Transductive Learning

 Learning Techniques

10.Multi-Task Learning 

11.Active Learning

12.Online Learning

13.Transfer Learning

14.Ensemble Learning 

Evaluation Metrics for Classification

A. Binary Classification

Confusion Matrix

The confusion matrix is a table with the number of correct and incorrect predictions broken down by class