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13. External Resources, Videos and Talks
New to Scientific Python?: For those that are still new to the scientific Python ecosystem, we highly recommend the Python Scientific Lecture Notes. This will help you find your footing a bit and w......
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3.3. Tuning the decision threshold for class prediction
Classification is best divided into two parts: the statistical problem of learning a model to predict, ideally, class probabilities;, the decision problem to take concrete action based on those pro......
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11.1. Array API support (experimental)
The Array API specification defines a standard API for all array manipulation libraries with a NumPy-like API. Some scikit-learn estimators that primarily rely on NumPy (as opposed to using Cython)......
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1.1. Linear Models
The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the features. In mathematical notation, if\hat{y} is the predicted val......
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1.2. Linear and Quadratic Discriminant Analysis
Linear Discriminant Analysis ( LinearDiscriminantAnalysis) and Quadratic Discriminant Analysis ( QuadraticDiscriminantAnalysis) are two classic classifiers, with, as their names suggest, a linear a......
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1.3. Kernel ridge regression
Kernel ridge regression (KRR)[M2012] combines Ridge regression and classification(linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the sp......
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2.3. Clustering
Clustering of unlabeled data can be performed with the module sklearn.cluster. Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on trai......
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2.5. Decomposing signals in components (matrix factorization problems)
Principal component analysis (PCA): Exact PCA and probabilistic interpretation: PCA is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum a......
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2.6. Covariance estimation
Many statistical problems require the estimation of a population’s covariance matrix, which can be seen as an estimation of data set scatter plot shape. Most of the time, such an estimation has to ......
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2.7. Novelty and Outlier Detection
Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations (it is an inlier), or should be considered as different (it is an ......
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2.8. Density Estimation
Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as......
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3.1. Cross-validation: evaluating estimator performance
Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha......
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