# ML crash course - Datasets, generalization, and overfitting

> Machine learning crash course 중 datasets, generalization, and overfitting 챕터.

[Machine learning crash course](https://wiki.g15e.com/pages/Machine%20learning%20crash%20course.txt) 중 [datasets](https://wiki.g15e.com/pages/Dataset%20(machine%20learning.txt)), [generalization](https://wiki.g15e.com/pages/Generalization%20(machine%20learning.txt)), and [overfitting](https://wiki.g15e.com/pages/Overfitting.txt) 챕터.

https://developers.google.com/machine-learning/crash-course/overfitting

## Introduction

## Data characteristics

### Types of data

### Quantity of data

### Quality and reliability of data

### Complete vs. incomplete examples

## Labels

### Direct versus proxy labels

### Human-generated data

## Imbalanced datasets

### Downsampling and Upweighting

### Rebalance ratios

## Dividing the original dataset

### Training, validation, and test sets

### Additional problems with test sets

## Transforming data

## Generalization

## Overfitting

### Fitting, overfitting, and underfitting

### Detecting overfitting

### What causes overfitting?

### Generalization conditions

## Model complexity

### Regularization

### What is complexity?

## L2 regularization

### Regularization rate (lambda)

### Early stopping: an alternative to complexity-based regularization

### Finding equilibrium between learning rate and regularization rate

## Interpreting loss curves

## What's next?

- [ML crash course - Neural networks](https://wiki.g15e.com/pages/ML%20crash%20course%20-%20Neural%20networks.txt)