scikit learn algorithm cheat sheet

Reference: http://www.cnblogs.com/pinard/p/6056319.htmlBefore, the algorithm principle of decision tree was summarized, including the principle of decision tree algorithm (above) and the principle of decision tree algorithm (below). Today, we introduce the decision tree algorithm from the point of view of practice, mai

Scikit-learn provides a lot of class libraries for linear regression, which can be used to do linear regression analysis, This article summarizes the use of these libraries, focusing on the differences of these linear regression algorithm libraries and their respective usage scenarios.The purpose of linear regression is to obtain the linear relationship between t

Copyright NOTICE: Directory (?) [+]======================================================================This series of blogs mainly refer to the Scikit-learn official website for each algorithm, and to do some translation, if there are errors, please correct meReprint please indicate the source, thank you==========================================================

Copyright NOTICE: Directory (?) [+]======================================================================This series of blogs mainly refer to the Scikit-learn official website for each algorithm, and to do some translation, if there are errors, please correct me======================================================================The

Preface In this paper, how to use the KNN,SVM algorithm in Scikit learn library for handwriting recognition. Data Description: The data has 785 columns, the first column is label, and the remaining 784 columns of data store the pixel values of the grayscale image (0~255) 28*28=784 installation Scikit

Original address: Https://www.jiqizhixin.com/articles/2018-04-03-5K nearest neighbor algorithm, referred to as K-NN. In today's deep-learning era, this classic machine learning algorithm is often overlooked. This tutorial will take you to build the K-nearest neighbor algorithm using Scikit-

classifiers2.2 loss: {' ls ', ' lad ', ' Huber ', ' quantile '}, optional (default= ' ls ')Loss function2.3 learning_rate:float, Optional (default=0.1)The step length of SGB (random gradient Ascension) is also called learning speed, and the lower the learning_rate, the greater the N_estimators.Experience shows that the smaller the learning_rate, the smaller the test error; see http://scikit-learn.org/stable/modules/ensemble.html#Regularization for sp

1 ImportNumPy as NP2 fromSklearnImportDatasets#Data Set3 fromSklearn.model_selectionImportTrain_test_split#Train_test_split is used to divide data into training sets and test sets4 fromSklearn.neighborsImportKneighborsclassifier#inductive KNN algorithm5Iris = Datasets.load_iris ()#data from datasets to be loaded into Iris6Iris_x =Iris.data7Iris_y =Iris.target8X_train,x_test,y_train,y_test = Train_test_split (iris_x,iris_y,test_size=0.3)#split Training sets and test sets9KNN =Kneighborsclassif