Have an interesting problem?Lets talk Since I posted apostmortemof my entry to Kaggles See Click Fix competition, Ive meant to keep sharing things that I learn as I improve my machine learning skills. One that Ive been meaning to share isscikit-learns pipeline module. The following is a moderately detailed explanation and a few examples of […]

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The Sklearn library provides several powerful tools that can be used to extract features from text. In this article, I will show you how easy it can be to classify documents based on their content using Sklearn. from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer You will need to import pandas (of course) and CountVectorizer. […]

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Users who have contributed to this file KFold, train_test_split, GridSearchCV confusion_matrix, mean_squared_error load_iris, load_digits, load_boston Zeros and Ones from the Digits dataset: binary classification xgb.XGBClassifier().fit(X[train_index], y[train_index]) (confusion_matrix(actuals, predictions)) xgb.XGBClassifier().fit(X[train_index], y[train_index]) (confusion_matrix(actuals, predictions)) xgb.XGBRegressor().fit(X[train_index], y[train_index]) (mean_squared_error(actuals, predictions)) The sklearn API models are picklable must open in binary format to pickle (np.allclose(clf.predict(X), clf2.predict(X))) You cant perform that […]

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Thedecision treesfromscikit-learnare very easy to train and predict with, but its not easy to see the rules they learn. The code below makes it easier to see insideclassification trees, enabling visualizations that look like this: This shows, for example, that all theiriseswithpetal length (cm)less than 2.45 weresetosa. The ability to interpret the rules of a […]

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Assumes data is normal, mean 0, sd 1 applies to row such that l1 / l2 norm is 1 Transformed samples with class labels from matplotlib.mlab.PCA() from sklearn import preprocessing from sklearn.datasets import load_iris iris = load_iris() X = iris.data y = iris.target minmaxScale = preprocessing.MinMaxScaler().fit(X) Assumes data is normal, mean 0, sd 1 minmaxTransformed […]

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We examine how the popular framework sklearn can be used with the iris dataset to classify species of flowers. We go through all the steps required to make a machine learning model from start to end. Okay, so youre interested in machine learning.But you dont know where to start, or perhaps you have read some […]

Read More → Creating Your First Machine Learning Classifier with Sklearn

8.12.2. sklearn.kernel_approximation.AdditiveChi2Sampler This documentation is for scikit-learnversion 0.11-gitOther versions If you use the software, please considerciting scikit-learn. Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform. parameter of RBF kernel: exp(-gamma * x**2) number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space. […]

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sklearnsklearnAPI sklearnsklearnAPI scikit-learnPythonsklearn sklearn sklearntraining datatesting datamodel selectcross validationsklearn sklearn6 sklearnsklearnAPI sklearnIriskNNkNNkNN load_irisIrisIris4012 %pyspark from sklearn.datasets import load_iris from sklearn.cross_validation import train_test_split iris = load_iris() data_X = iris.data data_y = iris.target 3 print(data:, data_X.shape, data_y.shape) print(features:, data_X[:3, :]) print(target:, data_y[:3]) train_X, test_X, train_y, test_y = train_test_split(data_X, data_y, test_size=0.2) print(train:, train_X.shape, train_y.shape) print(test: , test_X.shape, test_y.shape) […]

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SklearnpythonSVMPCAsklearn.cluster class sklearn.cluster.AffinityPropagation(damping=0.5, max_iter=200, convit=30,copy=True) S classs klearn.cluster.DBSCAN : classs klearn.cluster.DBSCAN(eps=0.5, min_samples=5, metric=euclidean, random_state=None) core_sample_indiacrers[n_core_samples] components_shape =[n_core_samples,n_features] labels_ -1shape = [n_samples] class sklearn.cluster.KMeans(n_clusters=8, init=k-means++, n_init=10, max_iter=300,tol=0.0001, precompute_distances=True, verbose=0, random_state=None, copy_x=True, n_jobs=1, k=None) max_iter300 n_init:10 Initk-means++ precompute_distancesTrue n_jobs1-1CPU cluster_centers_: array, [n_clusters,n_features] (X, y=None)KXX (X)X: array-like, sparse matrix, shape = [n_samples, n_features] (X, y=None)X: array-like, sparse matrix, […]

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scikit-learnNumPySciPyMatplotlibPython knnSVMk-means KaggleKaggleKaggleDigitRecognitionkNNkNNscikit-learnscikit-learn DigitRecognitionDigitRecognitionKaggle DigitRecognitionKagglescikit-learnkNNkSVMNB12 Kaggle train.csv:trainDatatrainLabel loadTrainData()loadTestData() toInt()nomalizing() train.csvtest.csvfeaturelabelfeaturetrainDatatrainLabeltestData knnClassify(trainData,trainLabel,testData): default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10) knnClf.fit(trainData,ravel(trainLabel)) sklearn_knn_Result.csv kNNkk=5comments svcClassify(trainData,trainLabel,testData): default:C=1.0,kernel = rbf. you can try kernel:linear, poly, rbf, sigmoid, precomputed svcClf.fit(trainData,ravel(trainLabel)) sklearn_SVC_C=5.0_Result.csv SVC()rbf,C1.0 scikit,GaussianNBMultinomialNB GaussianNBClassify(trainData,trainLabel,testData): nbClf.fit(trainData,ravel(trainLabel)) sklearn_GaussianNB_Result.csv MultinomialNBClassify(trainData,trainLabel,testData): default alpha=1.0,Setting alpha = 1 is called Laplace smoothing, while alpha  1 is called Lidstone smoothing. nbClf.fit(trainData,ravel(trainLabel)) sklearn_MultinomialNB_alpha=0.1_Result.csv :alpha :[python] fit(X,y)X:  trainDataarray-like, shape = [n_samples, n_features]Xn_samplesn_featuresy:  trainLabelarray-like, shape = [n_samples]ynumpy.ravel()[python] Kagglemake a submission knn95.871%alpha=1.081.043%SVMlinear93.943%CSDNKaggle -scikit-learnDigitRecoginitionGithub[python] Created on Tue Dec 16 21:59:00 2014 nomalizing(toInt(data)),toInt(label) knnClassify(trainData,trainLabel,testData): default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10) knnClf.fit(trainData,ravel(trainLabel)) sklearn_knn_Result.csv svcClassify(trainData,trainLabel,testData): default:C=1.0,kernel = rbf. you can try kernel:linear, poly, rbf, sigmoid, precomputed svcClf.fit(trainData,ravel(trainLabel)) sklearn_SVC_C=5.0_Result.csv […]

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• machine learning, NLP, data mining • custom SW design, development, optimizations • corporate trainings & IT consulting Scikit learn interface for gensim for easy use of gensim with scikit-learn follows on scikit learn API conventions Bases:sklearn.base.TransformerMixin,sklearn.base.BaseEstimator Sklearn wrapper for LDA model. See gensim.model.LdaModel for parameter details. scorerspecifies the metric used in thescorefunction. Seegensim.models.LdaModelclass for […]

Read More → sklearn_apildamodel Scikit learn wrapper for Latent Dirichlet Allocation

Azure Machine Learning services (preview) is an integrated, end-to-end data science and advanced analytics solution for professional data scientists to prepare data, develop experiments, and deploy models at cloud scale. This tutorial is part two of a three-part series. In this part of the tutorial, you use Azure Machine Learning services (preview) to: Use Azure […]

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What is the advantage of using fit and then transform instead of fit_transform in sklearn? First of all, the difference between them has been mentioned here: what is the difference between transform and fit_transform in sklearn Fit and then transform, make it possible to fit on training data and transform on test data. So the […]

Read More → 2 Answers – What is the advantage of using fit and then transform instead of fit_transform in sklearn?

Ͷdaisy [] ͣת ʱ䣺2016-09-11Ҫ ܽǴpythonԤͨsklearnpreprocessingģ; 1. ׼Standardization or Mean Removal and Variance Scaling) ά0ֵҲz-scoreֵ㷽ʽǽֵȥֵԱ׼ һtraintestһ׼train׼ͬı׼ȥ׼testʱscaler -ԭʼݽԱ[0,1]䣨Ҳ̶ֵ䣩 ǽֵͬӳ䵽ͬĹ̶[0,1]ʱҲΪһ Էֶÿһ0.4^2+0.4^2+0.81^2=1,L2 normÿĸάƽΪ1ƵأL1 normDZÿĸάľֵΪ1max normǽÿĸάԸάֵ ڶ֮ʱʹõǶkernelҪNormalization ʱ͵ģһ㷨ֵͣʱҪ롣 enc = preprocessing.OneHotEncoder() enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) enc.transform([[0, 1, 3]]).toarray() array([[ 1., 0., 0., 1., 0., 0., 0., 0., 1.]]) ӣһάֵ01ȥ롣ڶάά le = sklearn.preprocessing.LabelEncoder() […]

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Train the model using libsvm (low-level method) X: array-like, dtype=float64, size=[n_samples, n_features] Y: array, dtype=float64, size=[n_samples] Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR respectively. 0 by default. kernel: linear, rbf, poly, sigmoid, precomputed, optional Kernel to use in the model: linear, polynomial, RBF, sigmoid or precomputed. rbf by default. Degree of the polynomial […]

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This documentation is for scikit-learn If you use the software, please considerciting scikit-learn. “sklearn.preprocessing“ ** scikit ** ** ** ,(non-constant features) , (L1L2) :func:scale: preprocessing“:class:`StandardScaler`,“Transformer. This class is hence suitable for use in the early steps of asklearn.pipeline.Pipeline: :class:StandardScaler“`with_mean=False“`with_std=False“ [0,1] :class:`MinMaxScaler`:class:`MaxAbsScaler` The motivation to use this scaling include robustness to very small standard deviations of […]

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pythonsklearn perceptron weightweight vectorbias sklearn sklearnmake_classification from sklearn.datasets import make_classification x,y = make_classification(n_samples=1000, n_features=2,n_redundant=0,n_informative=1,n_clusters_per_class=1) n_samples: n_features=2:=n_informative + n_redundant + n_repeated n_informative n_redundantinformative n_clusters_per_class cluster make_classificationxy10 x_data_train = x[:800,:] x_data_test = x[800:,:] y_data_train = y[:800] y_data_test = y[800:] positive_x1 = [x[i,0] for i in range(1000) if y[i] == 1] positive_x2 = [x[i,1] for i in range(1000) […]

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You can find an executable version of this example in In this example, we will train an SVC with RBF kernel using scikit-learn. In this case, we have to tune two hyperparameters:Candgamma. We will use twice iterated 10-fold cross-validation to test a pair of hyperparameters. In this example, we will useoptunity.maximize(). Enter search terms or […]

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sklearn.preprocessing.StandardScaler.fit_transform Example 2017227 – scikit-learn,,,,fit,fit_transform,transformss=StandardScaler() X… 2016718 – scaler = sklearn.preprocessing.StandardScaler().fit(train) scaler.transform(train) scaler.transform(test),… ()— – Charlotte77 – 2016628 – 1.2 StandardScaler—, 1 scaler = preprocessing.StandardScaler().fit(X) 2 out: 3 Stan… python – slearn standard scaler transform VS fit_transform … sklearn //–… 201561 – scaler = preprocessing.StandardScaler().fit(X) scaler StandardScaler(copy=True, with_mean=True, with_std=True) an… scikit-learn(preprocessing) – cherish_z…_ 201522 – […]

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KDnuggets HomeNews2016AugTutorials, Overviews Contest Winner: Winning the AutoML Challenge with Auto-sklearn (16:n29)Contest Winner: Winning the AutoML Challenge with Auto-sklearn http likes 45Tags:AutomatedAutomated Data ScienceAutomated Machine LearningCompetitionHyperparameterscikit-learnWeka This post is the first place prize recipient in the recent KDnuggets blog contest. Auto-sklearn is an open-source Python tool that automatically determines effective machine learning pipelines for classification […]

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