best machine learning library

python is an object-oriented, interpretive computer programming language with a rich and powerful library, coupled with its simplicity, ease of learning, speed, open source free, portability, extensibility, and object-oriented features,python Become the most popular programming language of the 2017! AI is one of the hottest topics, and machine

Shark is a fast, modular, feature-rich, open-source C + + machine learning Library. It realizes linear and nonlinear optimization, learning algorithm based on kernel function, neural network and many other machine learning algorit

When learning machine learning, we basically use MATLAB and python to write algorithms and perform tests; Recently, thanks to the use of opencv to write homework, we have taken a look at the Machine Learning Library (MLL) of openc

=NewVectorassembler (). Setinputcols (Array ("Square"). Setoutputcol ("Features") Val DataSet=assembler.transform (data) var Array (train,test)=dataset.randomsplit (Array (0.8,0.2), 1234L) Train.show () println (Test.count ()) var regression=NewLinearregression (). Setmaxiter () Setregparam (0.3). Setelasticnetparam (0.8) Val Model=regression.setlabelcol ("Price"). Setfeaturescol ("Features"). Fit (train) model.transform (test). Show () Val s=model.summary.totalIterations println (s"ITER: ${s}")

Forest 40g Maximum entropy 40g Decision Tree 40g BAGGING 40g Svm 20% Experiment two (code file Sentiment_analyse. R):Data file: http:///sentiment/data/Classification using Bayes, MAXENT, SVM, Slda, BAGGING, RF, tree classifierThe results are as follows: Classifier Name Accuracy rate (R) Accuracy rate (spark) Bayesian 65% 85D Random Forest

!accuracy:87.07%******************* SVM ********************Training took3831. 564000s!accuracy:94.35%******************* GBDT ********************In this data set, because the cluster of data distribution is better (if you understand this database, see its T-sne map can be seen.) Since the task is simple, it has been considered a toy dataset in the deep learning boundary, so KNN has a good effect. GBDT is a very good algorithm, in Kaggle and other bi

Python Machine Learning Library sciki-earn practice, pythonsciki-earn Use Anaconda's spyder: Create train_test.py #!usr/bin/env python #-*- coding: utf-8 -*- import sys import os import time from sklearn import metrics import numpy as np import cPickle as pickle reload(sys) sys.setdefaultencoding('utf8') # Multinomial Naive Bayes Classifier def n

sklearn.neighbors import Kneig Hborsclassifier import time if __name__ = = "__main__": train_num = 20000 Test_num = 30000 data = Pd.read_csv (' Train.csv ') Train_data = data.values[0:train_num,1:] Train_label = data.values[0:train_num,0] Test_data = data . values[train_num:test_num,1:] Test_label = data.values[train_num:test_num,0] t = time.time () Pca=PCA (n_compo nents = 0.8) train_x = Pca.fit_transform (train_data) test_x = Pca.transform (test_data) neighbors = KNeighborsC Lassifier (n_neig

are slightly different, and many very small elements are left in the matrix, which results from the computer processing error. Enter the following command to get the error value:>>> MyEye = Randmat*invrandmat>>> Myeye-eye (4)Matrix ([[ 0.00000000e+00, -4.44089210e-16, -4.44089210e-16, -3.33066907E-16], [ -8.88178420e-16, 2.22044605e-16, 0.00000000e+00, 5.55111512E-17], [ 4.44089210e-16, 0.00000000e+00, 0.00000000e+00, -5.55111512E-17],

Installation sudo yum install NumPy From numpy Import * Produces an array Random.rand (4,5) Result Array ([[0.79056842, 0.31659893, 0.34054779, 0.97328131, 0.32648329], [0.51585845, 0.70683055, 0.31476985, 0.07952725, 0.80907845], [0.81623517, 0.61038487, 0.66679161, 0.77412742, 0.03394483], [0.41758993, 0.54425978, 0.65350633, 0.90397197, 0.72706079]]) Produce a matrix >>> Randmat=mat (Random.rand (bis)) >>> randmat.i Matrix ([[[1.72265179, 0.82071484, 0.8218207,-3.20005387], [0.60602642,-1.28

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit that, as a beginner, may not be in the early st

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

What is machine learning?"Machine learning" is one of the core research fields of artificial intelligence, its initial research motive is to let the computer system have human learning ability to realize artificial intelligence.In fact, since "experience" is mainly in the fo

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-