# Model Visualisation

### With Python and R Code

Though visualisation is used in data science to understand the shape of the data (data-vis), it is not widely used for the models developed; which are largely evaluated based on numerical summaries. Model visualisation (model-vis) can help understand: the shape of the model, the impact of parameters & different input data on the model, the fit of the model & where it can be improved.

## 1. Introduction

## 2. Learning and Layers

## 3. n/p/N Challenge

## 4. Regression: Small

(p = 10, n < 1K)

## 5. Regression: N Models

(p = 10, n < 10K, model = 50)

## 6. Classification: 2 Class

(p = 10, n < 100K, class = 2)

## 7. Classification: 10 Class

(p = 10, n < 100K, class = 10)

## 8. Classification: Large

(p = 10, n ~ 1M)