import scikit learn

, the following look at the effect of each algorithm, on Kaggle make a submissionthe effect of KNN algorithm, accuracy rate 95.871%Naive Bayes, alpha=1.0, accuracy rate 81.043%SVM. Linear nucleus. Accurate rate 93.943%3. Project filecsdn Download: Kaggle Get started-use Scikit-learn to resolve digitrecoginitiongithub:https://github.com/wepe/kaggle-solutionpost the code:#!/usr/bin/python#-*-coding:utf-8-*-""

Python series-install pip and scikit-learn, pipscikit-learn Server environment: Centos7.0 Python2.7.5 Pip-6.1.1 Citation: many things are simplified due to the rush of cluster configuration. Many template libraries such as python are not installed. Today, a lab engineer is using a server to run programs and code written in python. Many library files are not

def load_data (): X1_tra in = Np.linspace (0,50,500) X2_train = Np.linspace ( -10,10,500) data_train = Np.array ([[X1,x2,f (X1,X2) + ( Np.random.random (1) -0.5)] x1,x2 in Zip (X1_train, X2_train)]) x1_test = Np.linspace (0,50,100) + 0.5 * Np.random.random (+) x2_test = Np.linspace ( -10,10,100) + 0.02 * Np.random.random (+) data_test = Np.array ([[ X1,x2,f (X1,X2)] for x1,x2 in Zip (X1_test, x2_test)]) return data_train, Data_test The image of the training set (th

) Call The kNN algorithm in scikit-learn. # Call The knn algorithm package of scikit from sklearn. neighbors import into def knnClassify (trainData, trainLabel, testData): knnClf = encrypt () # default: k = 5, defined by yourself: KNeighborsClassifier (n_neighbors = 10) knnClf. fit (trainData, ravel (trainLabel) testLa

Scikit-learn Getting Started-Xuan Sen2. Installing the SoftwarePython 2.0 I recommend using the "pip install scikit-learn" or "easy_install scikit-learn" fully automatic installation, and then through "From sklearn

\) value case, run n times, and then compare the results of the pros and cons of the degree? You can do this, but Scikit-learn provides another API for cross-validation to choose the optimal \ (\alpha\), and we use this API to select \ (\alpha\).5. Select Ridge Regression parameter with Scikit-learn \ (\alpha\)Here we

Required to go directly to the successful installation processTrial and Error CourseI recently saw the Scikit-learn library, I think in Python called to do some testing, and so very convenient to start configuration, As a result of the previous installation of python2.7, it is intended to be configured in the previous version, from the online various posts to try the method, finally prompted to install the

Recently began to learn to use Scikit-learn, every day to write about what you learned, not only can remind yourself what to learn a day, but also convenient to review.Install Scikit-learn on my virtual machine Ubuntu, the install

, yy, Z, alpha=0.4) Plt.scatter (x[:, 0], x[:, 1], c=y, alpha=0.8) plt.show ()The resulting figure is as follows:We then visualized our decision tree, using the recommended third method. The code is as follows:From Ipython.display import Image from sklearn import treeimport pydotplus dot_data = Tree.export_graphviz (CLF, Out_f Ile=none, feature_names=iris.feature_names,

true, the data is not modified if it is copied. BOOL will have this parameter in many Scikit-learn interfaces, that is, whether to continue the copy operation on the input data so that the user's input data is not modified. This understanding of Python's memory mechanism will be more clear. N_jobs: Parallel settings Algorithm:kmeans implementation algorithm, there are: ' Auto ', ' full ', ' Elkan '

, test_size=0.5,random_state= Seed_i)Regressionfunc_2.fit (X_train_m,y_train_m)Sco=regressionfunc_2.score (X_test_m,y_test_m, Sample_weight=none)Gridsearch:From Sklearn.grid_searchimport GRIDSEARCHCVTuned_parameters =[{' penalty ': [' L1 '], ' tol ': [1e-3, 1e-4],' C ': [1, 10, 100, 1000]},{' Penalty ': [' L2 '], ' tol ': [1e-3, 1e-4],' C ': [1, 10, 100, 1000]}CLF =GRIDSEARCHCV (Logisticregression (), Tuned_parameters, cv=5, scoring=[' precision ', ' recall '])Print (CLF.BEST_ESTIMATOR_)Of cours

Scikit-learn this very powerful Python machine learning ToolkitHttp://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.htmlS1. Import dataMost of the data is formatted as M n-dimensional vectors, divided into training sets and test sets. So, knowing how to import ve

Update a library with the PIP commandPip Install--upgrade library namesuch as updating the Scikit-learn packagePip Install--upgrade Scikit-learnCannot import name Mlpclassifier workaround:Scikit-Learn v0.17 only BERNOULLIRBM, no mlpclassifier. Only needto upgrade

Python Open Source Toolkit: Scikit-learn is a development kit for machine learning, home: http://scikit-learn.org/stable/index.htmlThis package to the classic machine learning algorithms are implemented using Python, is learning machine learning very good theory and practice of combining materials, but in the installation of