Python for Data Science For Dummies, 2nd Edition
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Scikit-learn is a focal point for data science work with Python, so it pays to know which methods you need most. The following table provides a brief overview of the most important methods used for data analysis.
Syntax Usage Description
model_selection.cross_val_score Cross-validation phase Estimate the cross-validation score
model_selection.KFold Cross-validation phase Divide the dataset into k folds for cross validation
model_selection.StratifiedKFold Cross-validation phase Stratified validation that takes into account the distribution of the classes you predict
model_selection.train_test_split Cross-validation phase Split your data into training and test sets
decomposition.PCA Dimensionality reduction Principal component analysis (PCA)
decomposition.RandomizedPCA Dimensionality reduction Principal component analysis (PCA) using randomized SVD
feature_extraction.FeatureHasher Preparing your data The hashing trick, allowing you to accommodate a large number of features in your dataset
feature_extraction.text.CountVectorizer Preparing your data Convert text documents into a matrix of count data
feature_extraction.text.HashingVectorizer Preparing your data Directly convert your text using the hashing trick
feature_extraction.text.TfidfVectorizer Preparing your data Creates a dataset of TF-IDF features
feature_selection.RFECV Feature selection Automatic feature selection
model_selection.GridSearchCV Optimization Exhaustive search in order to maximize a machine learning algorithm
linear_model.LinearRegression Prediction Linear regression
linear_model.LogisticRegression Prediction Linear logistic regression
metrics.accuracy_score Solution evaluation Accuracy classification score
metrics.f1_score Solution evaluation Compute the F1 score, balancing accuracy and recall
metrics.mean_absolute_error Solution evaluation Mean absolute error regression error
metrics.mean_squared_error Solution evaluation Mean squared error regression error
metrics.roc_auc_score Solution evaluation Compute Area Under the Curve (AUC) from prediction scores
naive_bayes.MultinomialNB Prediction Multinomial Naïve Bayes
neighbors.KNeighborsClassifier Prediction K-Neighbors classification
preprocessing.Binarizer Preparing your data Create binary variables (feature values to 0 or 1)
preprocessing.Imputer Preparing your data Missing values imputation
preprocessing.MinMaxScaler Preparing your data Create variables bound by a minimum and maximum value
preprocessing.OneHotEncoder Preparing your data Transform categorical integer features into binary ones
preprocessing.StandardScaler Preparing your data Variable standardization by removing the mean and scaling to unit variance

About This Article

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About the book authors:

John Paul Mueller is a tech editor and the author of over 100 books on topics from networking and home security to database management and heads-down programming. Follow John's blog at http://blog.johnmuellerbooks.com/. Luca Massaron is a data scientist who specializes in organizing and interpreting big data and transforming it into smart data. He is a Google Developer Expert (GDE) in machine learning.

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