3. Optimization Models - Introduction

2. Introduction to Spread Sheet Modeling - Continued

1. Introduction

Model Selection

MultiClass Classification Evaluation

Classification Metrics: F1 Score, Classification Report, Specificity, AUC-ROC, and LogLoss

Other Classification Metrics: Error Precision and Recall

Classification Metrics: Confusion Matrix

Evaluation using a Dummy Model

Evaluating a Classification Model

Resampling Techniques

Deep Learning

Artificial Neural Networks - Put it into work

Artificial Neural Networks

Gradient Boosting Decision Trees - Put it into work

Gradient Boosting Decision Trees

Random Forests Put it into work

Random Forests

Decision Trees - Put it into work

Decision Trees

Naive Bayse - Put it into work

Naive Bayse

Support Vector Machines - Put it into work

Support Vector Machines (SVMs)

Logistic Regression - Put it into work

Classification Algorithms - Logistic Regression

K Nearest Neighbors - Put it into work

Classification Algorithms: K Nearest Neighbors

Feature Scaling: Put it into work

Feature Scaling