Learning scikit-learn: Machine Learning in Python
The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Downloading theexample code
Other editions - View all
all_tasks alpha attributes Chapter classifier clf.fit(X_train coefficient of determination coefficients computation confusion matrix CountVectorizer crossvalidation curse of dimensionality cv_split_filename dataset decision trees default dimensionality dimensionality reduction distribution email evaluate example f1score Feature selection feature_names function grid search handwritten digits hyperparameters hyperplane images import matplotlib.pyplot Installing scikitlearn inthe iterator KFold kmeans learning algorithms linear classification machine learning method matplotlib Mean score measure model selection module MultinomialNB Naïve Bayes number of features number of instances numpy obtain ofthe overfitting Packt parameter values performance pip install Pipeline plot predict Principal Component Analysis print Accuracy problem Python Rand index Random Forests random_state=0 realvalued recall regression scikitlearn sepal setosa SGDClassifier sklearn import metrics sklearn.cross_validation import train_test_split sklearn.datasets import supervised learning Support Vector Machines svc__C svc__gamma target class test_scores testing set TfidfVectorizer titanic_X tothe training data training instances training set twodimensional versicolor wewill X_train_fs y_max y_pred y_test y_train