effect of data representation choices on evaluation and improvement - evaluation metrics and scoring - iris classification application - overfitting vs underfitting pipeline preprocessing and selecting selecting with grid search theory behind tuning parameters with grid search - movie reviews multiclass classification vs binary classification evaluation metrics and scoring for - linear models for uncertainty estimates multilayer perceptrons (mlps) multinomialnb -grams naive bayes classifiers kinds in scikit-learn parameters strengths and weaknesses natural language processing (nlp) negative class nested cross-validation netflix prize challenge neural networks (deep learningaccuracy of estimating complexity in predictions with randomization in recent breakthroughs in strengths and weaknesses tuning non-negative matrix factorization (nmfapplications for applying to face images applying to synthetic data normalization normalized mutual information (nmi) numpy (numeric pythonlibrary offline evaluation one-hot-encoding - index one-out-of- encoding - one-vs -rest approach online resourcesix online testing openml platform operating points ordinary least squares (ols) out-of-core learning outlier detection overfitting pair plots pandas benefits of checking string-encoded data column indexing in converting data to one-hot-encoding get_dummies function parallelization over cluster permissionsx pipelines (see algorithm chains and pipelinespolynomial features - polynomial kernels polynomial regression positive class posix time preand post-pruning precision precision-recall curves - predict for the future approach predict method predict_proba function preprocessing - data transformation application effect on supervised learning kinds of parameter selection with pipelines and purpose of scaling training and test data principal component analysis (pcadrawbacks of example of feature extraction with unsupervised nature of visualizations with whitening option probabilistic modeling