scikit learn classifiers

']X_new_counts =count_vect.transform (docs_new) x_new_tfidf=tfidf_transformer.fit_transform (X_new_ Counts) predicted=clf.predict (X_NEW_TFIDF) fordoc,categoryinzip (Docs_new, predicted):print '%r=>%s ' % (doc,twenty_train.target_ Names[category]Categorize 2,257 of documents in Fetch_20newsgroups Count the occurrences of each word With TF-IDF statistics, TF is the number of occurrences of each word in a document divided by the total number of words in the document, IDF is the total

data.X = [[1.,-1., 2.], [2., 0., 0.], [0.,1.,-1.]] Binarizer= preprocessing. Binarizer (). Fit (X)#The default threshold value is 0.0PrintBinarizer#Binarizer (copy=true, threshold=0.0)Printbinarizer.transform (X)#[1.0. 1.]#[1.0. 0.]#[0.1. 0.]Binarizer= preprocessing. Binarizer (threshold=1.1)#set the threshold value to 1.1Printbinarizer.transform (X)#[0.0. 1.]#[1.0. 0.]#[0.0. 0.]4. Label preprocessing (label preprocessing)4.1) Label binary value (label binarization)Labelbinarizer is typica

classifier:Articles that need to be categorized are placed in the Predict_data directory: still an article a TXT file#-*-coding:utf-8-*-# @Time: ./8/ at -: Geneva# @Author: Ouch # @Site: # @File: Bayesian classifier. py# @Software: Pycharm import reimport jiebaimport json fromsklearn.datasets Import Load_files fromsklearn.feature_extraction.text import Countvectorizer, Tfidftransformer fromsklearn.externals Import joblib # load classifier CLF= Joblib.load ('MODEL.PKL') Count_vect= Joblib.load

regression or nonlinear regression, is not as rich as the information contained in the model tree, so the model tree has higher prediction accuracy. Scikit-learn Implementation #!/usr/bin/python # Created by Lixin 20161118 import numpy as NP- numpy import * from sklearn.tree imp ORT decisiontreeregressor import Matplotlib.pyplot as PLT def plotfigure (X,X_TEST,Y,YP): plt.figure () Plt.sca

GRIDSEARCHCV function to automatically find the optimal alpha value: From Sklearn.grid_search import GRIDSEARCHCV GSCV = GRIDSEARCHCV (Model (), Dict (Alpha=alphas), cv=3). Fit (X, y) Scikit-learn also provides an inline CV model, such as From Sklearn.linear_model import Ridgecv, LASSOCV Model = RIDGECV (Alphas=alphas, cv=3). Fit (X, y)This method can get the same result as GRIDSEARCHCV, but if it

function, except kernel= ' sigmoid ' effect is poor, the other effect is not very different.Then there is the training and testing session, where it divides all the data into two parts. Half to do the training set, half to do the test set.Let's talk about the parameters of the test here. The first is Precision,recall,F1-score, support these four parameters.F1-score is through Precision,recall the two are counted. formulas such as:Support is the supporting degree, which indicates the number of

Words don't say much, directly on the code 1 Code implementation and results screenshot, #coding: Utf-8#使用skflow内置的LR, the integrated regression model in Dnn,scikit-learn predicts "US Boston house prices"From Sklearn import datasets,metrics,preprocessing,cross_validation#读取数据Boston=datasets.load_boston ()#获取房价数据特征及对应房价X,y=boston.data,boston.target#数据分割, 25% tests.X_train,x_test,y_train,y_test=cross_validati

Because of the recent intention to learn "machine learning combat" this book, so using Python may be used NumPy, matplotlib, scikit-learn These libraries, so the Internet to find how to install these libraries, look at a number of methods, after trying to find themselves very lucky, Soon it's done, and it's not complicated. Let's get down to business! 1, to th