## Bagging (Random Forest)

1.Bagging and Bootstrap
Given a training set $X=x_{1},x_{2},…x_{n}$ with responses $Y=y_{1}, y_{2},.. y_{n}$, bagging repeatedly with K times, each time selects a subset of random samples with replacement from the training set and fits the algorithms like trees to these samples. Assuming we have trained a regression tree $f_{k}$ on $X_{k}$ and $Y_{k}$ where k=1…K. Predictions of unknown samples $X_{unknown}$ are based on the average of the predictions from all the trees $f_{k}$ on $X_{unknown}$.

Or as for classification trees, based on the majority votes of trees. The optimal number of trees K can be found through cross-validation or by out of bag error.

2.Decision Tree
A decision treee is a tree where each node represents a feature, each branch represents a dicision and each leaf represents an outcome. Tree models where the outcome are discrete values are called classification trees and tress models where the outcome take continuous values are called regression trees.

3.Random Forest
Random forest selects a random subset of the features at each candidate split in the learning process. If one or a few features are very strong predictors for the response variable, these features will be selected in many of the K trees, which cause them to become correlated.
I will use the package in python and house price data in Kaggle as an implemention example.

4.Implementation

## Boosting (Gradient Boosting)

1.Algorithm
The aim of the regression is to fit a model F(x) to predict the outcome $\hat{y}$ by minimizing the error. For example, in the situation of least-square regreesion setting, the aim of the regression is to fit a model F(x) to predict the outcome $\hat{y}$ by minimizing the mean squared error $\frac{1}{n} \sum_{i}(\hat{y_{i}}-y_{i})^2$.

At each stage k, $1 \leq k \leq K$, the function at each stage is $F_{k}$. The gradient boosting improves on the model by constructing a new model to estimate the error: $F_{k+1}(x)=F_{k}(x)+h(x)$. Assuming $\hat{y}=F(x)$ and the loss function is $L(y,\hat{y})$. The error for all samples are $J=\sum_{i=1}^{n} L(y_i,\hat{y_i}$. Based on the chapter of algebra basic, the gradient of J is $\nabla J=\hat{y} -y$. Therefore the algorithm of gradient boosting is:
1.initialize a model $F_{0}(x)$
2.For k=1 to K:
2.2Fit a function $h_{k}$ by using the training dataset
2.3compute $\gamma$ by solving the below equation: