loading the data into the model>>> import numpy as np >>> X = np.random.random((9,6)) >>> y = np.array(['M','M','m','f','m','m','f','M','F','na','m']) >>> X[X < =0.2] = 0 Estimators used in unsupervised learning models
>>> from sklearn.cluster import KMeans >>> k_means = KMeans(n_clusters=3, random_state=0) Model Fitting>>> k_means.fit(X_train) >>> pca_model = pca.fit_transform(X_train) Prediction Regression Metrics
>>> from sklearn.metrics import mean_absolute_error >>> y_true = [3, -0.5, 2]) >>> mean_absolute_error(y_true, y_pred)) |
Data pre-processing
>>> from sklearn.preprocessing import Normalizer >>> scaler = Normalizer().fit(X_train) >>> normalized_X = scaler.transform(X_train) >>> normalized_X_test = scaler.transform(X_test) Binarization technique>>> from sklearn.preprocessing import Binarizer >>> binarizer = Binarizer(threshold=0.0).fit(X) >>> binary_X = binarizer.transform(X) Attributing Missing Values>>>from sklearn.preprocessing import Imputer >>>imp = Imputer(missing_values=0, strategy='mean', axis=0) >>>imp.fit_transform(X_train) Performance metrics
>>> knn.score(X_test, y_test) >>> from sklearn.metrics import accuracy_score >>> accuracy_score(y_test, y_pred) Confusion Matrix>>> from sklearn.metrics import confusion_matrix >>> print(confusion_matrix(y_test, y_pred))) Model Tuning
>>> from sklearn.grid_search import GridSearchCV >>> params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]} >>> grid = GridSearchCV(estimator=knn,param_grid=params) >>> grid.fit(X_train, y_train) >>> print(grid.best_score_) >>> print(grid.best_estimator_.n_neighbors) |
Estimators used in supervised learning models
>>> from sklearn.linear_model import LinearRegression >>> lr = LinearRegression(normalize=True) Support Vector Machines (SVM)>>> from sklearn.svm import SVC >>> svc = SVC(kernel='linear') K- nearest neighbors>>> from sklearn import neighbors >>> knn = neighbors.KNeighborsClassifier(n_neighbors=5) |
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Cheatography
https://cheatography.com
Sci-Kit learn Cheat Sheet (DRAFT) by saicharan
Sci-kit is one of the advanced libraries in python. These are mainly used to develop supervised and unsupervised learning models.Through sci-kit library, it is easy to import the data set, eliminate the noise nand analyze the performance of the model.
This is a draft cheat sheet. It is a work in progress and is not finished yet.
