What is the random forest process of Python artificial intelligence algorithm?

Random Forest

(Random Forest) is an ensemble learning algorithm based on decision trees (explained earlier), which can handle both classification and regression problems.

The basic idea of ​​random forest is to generate multiple decision trees by randomly selecting samples and features, and then obtain the final result by taking a majority vote (classification problem) or mean calculation (regression problem). Specifically, the training process of random forest can be divided into the following steps:

• First, randomly select a certain number of samples from the original data set to form a new training set

• Randomly select a certain number of features from all features as candidate features for the node

• ## Use the above training set and candidate features to generate a decision tree

• Repeat steps 1-3 multiple times to generate multiple decision trees

• For classification problems, each leaf inside each decision tree The nodes all represent a category, and the final result is a majority vote; for regression problems, the final result is the average of all decision tree outputs

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd
# 读取数据
data = pd.read_csv('data.csv')
# 划分训练集和测试集
train, test = train_test_split(data, test_size=0.3)
# 提取训练集特征和标签
train_x = train.drop(columns=['label'])
train_y = train['label']
# 构建随机森林模型
rf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0)
# 拟合模型
rf.fit(train_x, train_y)
# 提取测试集特征和标签
test_x = test.drop(columns=['label'])
test_y = test['label']
# 预测并计算准确率
pred_y = rf.predict(test_x)
accuracy = accuracy_score(test_y, pred_y)
print("Accuracy:", accuracy)

When implementing the code, you first need to import the required library. Then, read in the data and divide it into a training set and a test set. Subsequently, the features and labels of the training set are extracted and a random forest model is built based on these data. After fitting the model, extract the features of the test set, use the model to predict, and calculate the prediction accuracy.

Summary of advantages and disadvantages

As an ensemble learning algorithm based on decision trees, it has the following advantages:

• It has high accuracy and Better robustness

• Can handle high-dimensional data without the need for feature selection

• Can evaluate each feature for classification/ The degree of impact of regression

• has excellent effect on processing large data sets.

• Randomization technology can reduce overfitting.

• can be used to evaluate important variables and features.

• The calculation speed is relatively fast.

There are advantages but also disadvantages:

• When processing large-scale data, the training time and space complexity are high

• For some special cases (such as data with highly correlated features), the performance of random forest may be poor

• The random forest model is not suitable for noise and anomalies Value data is prone to overfitting.

• The processing effect for unbalanced data sets is not good.

• The results of the random forest model are difficult to interpret.

• The storage and computing requirements for training data are relatively large.

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