Python machine learning in English

Supervised learning, supervised learning

Unsupervised learning, unsupervised learning

Category, Classificat

return, regression

dimensionality reduction, dimensionality reduction

Cluster, clustering

eigenvector, feature vector

compiler language, complied languages

Interpretive language, interpreted languages

Interpreter, interpreter

Boolean value, Boolean

Tuples, tuple

Arithmetic operations, arithmetic operators

Comparison operation, comparison operators

Assignment operation, assignment operators

Logical operation, logical operators

Member operations, menbership operators

Two categories, binary classification

Multi-classification, Multiclass classification

Multi-label classification, multi-lable classification

Linear classifier, linear classification

coefficient, coefficient

Intercept, intercept

parameters, Parameters

Random gradient rise, stochastic gradient ascend (SGA)

Forecast results, predicted condition

Correct tag, true condition

Confusion Matrix, confusion matrix

accuracy, accuracy

Recall rate, recall

Accurate rate, precision

Stochastic gradient descent model, SGD Classifier

Support Vector machine classifier, supported vector classifier

Naive Bayes, Naive Bayes

K Nearest neighbor classifier, Kneighborsclassifier

No parametric model, nonparametric models

Information entropy, information gain

Gini impure, Gini impurity

Integration, Ensemble

Single decision trees, decision tree

Random forest classifier, random forest classifier

Gradient boost decision tree, gradient tree boosting

Average absolute error, mean absolute error (MAE)

Mean square error, mean squared error (MSE)

Extreme random forest, extremely randomized trees

Random regression forest, randomforestregressor

Extreme return to the forest, Extratreesregressor

Nuclear function, kernal

Scikit-learn

Regression prediction capability rankings for house price forecasts, r-squared (a percentage that measures the volatility of model regression results to be verified by real values, and also implies the ability of the model to be numerically regressive)

1,gradient boosting regressor,0.8426

2,extra Trees regressor,0.8195

3,random Forest regressor,0.8024

4,SVM regressor (RBF kernel), 0.7564

5,KNN regressor (distance-weighted), 0.7198

6,decision Tree regressor,0.6941

7,KNN regressor (uniform-weighted), 0.6903

8,linear regressor,0.6763

9,sgdregressor,0.6599

10,SVM regressor (linear kernel), 0.6517

11,SVM regressor (poly kernel), 0.4045

Generalization force, generalization

Regularization, regularization

Over fitting, overfitting

Leave a verification, leave-one-out cross validation

Cross-validation, K-flod cross-validation

Python machine learning in English