Machine Learning with R
1
Prerequisites
1.1
Pre-requisite and conventions
1.2
Organization
1.3
Packages
2
Tests and inferences
2.1
Assumption of normality
2.1.1
Visual check of normality
2.1.2
Normality tests
2.2
T-tests
2.3
ANOVA - Analyse of variance.
2.4
Covariance
3
Single & Multiple Linear Regression
3.1
Single variable regression
3.2
Multi-variables regression
3.2.1
Predicting wine price (again!)
3.3
Model diagnostic and evaluation
3.4
Final example - Boston dataset - with backward elimination
3.4.1
Model diagmostic
3.5
References
4
Logistic Regression
4.1
Introduction
4.2
The logistic equation.
4.3
Performance of Logistic Regression Model
4.4
Setting up
4.5
Example 1 - Graduate Admission
4.6
Example 2 - Diabetes
4.6.1
Accounting for missing values
4.6.2
Imputting Missing Values
4.6.3
ROC and AUC
4.7
References
5
Softmax and multinomial regressions
5.1
Multinomial Logistic Regression
5.2
References
6
Gradient Descent
6.1
Example on functions
6.2
Example on regressions
7
KNN - K Nearest Neighbour
7.1
Example 1. Prostate Cancer dataset
7.2
Example 2. Wine dataset
7.2.1
Understand the data
7.3
References
8
Kmeans clustering
8.1
Multinomial Logistic Regression
8.2
References
9
Hierarichal Clustering
9.1
Example on the Pokemon dataset
9.2
Example on regressions
9.3
References
10
Principal Component Analysis
10.1
PCA on an easy example.
10.2
References.
11
Trees and Classification
11.1
Introduction
11.2
First example.
11.3
Second Example.
11.4
How does a tree decide where to split?
11.5
Third example.
11.6
References
12
Random Forest
12.1
How does it work?
12.2
References
13
Support Vector Machine
13.1
Support Vecotr Regression
13.1.1
Create data
13.1.2
Tuning a SVM model
13.1.3
Discussion on parameters
13.2
References
14
Model Evaluation
14.1
Biais variance tradeoff
14.2
Bagging
14.3
Cross Validation
15
Case Study - Text classification: Spam and Ham.
16
Case Study - Mushrooms Classification
16.1
Import the data
16.2
Tidy the data
16.3
Understand the data
16.3.1
Transform the data
16.3.2
Visualize the data
16.3.3
Modeling
16.4
Communication
17
Case study - The adults dataset.
17.1
Introduction
17.2
Import the data
17.3
Tidy the data
18
Case Study - Wisconsin Breast Cancer
18.1
Import the data
18.2
Tidy the data
18.3
Understand the data
18.3.1
Transform the data
18.3.2
Pre-process the data
18.3.3
Model the data
18.4
References
19
Final Words
References
Machine Learning with R
Chapter 14
Model Evaluation
14.1
Biais variance tradeoff
Biais Variance tradeoff
Model Complexity and total variance
14.2
Bagging
14.3
Cross Validation
