«Classification and regression tree analysis Beispiel» . «Classification and regression tree analysis Beispiel».

- API Reference — scikit-learn documentation
- Classification and regression - Spark Documentation
- Python - classification: PCA and logistic regression... - Stack Overflow
- Difference Between Classification and Regression (with Comparison...)
- _— scikit-learn...
- PowerPoint Presentation | Logistic Regression
- (PDF) Ensemble Classification and Regression-Recent...

“The Brier score is a proper score function that measures the accuracy of probabilistic predictions. It is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes.”

## API Reference — scikit-learn documentation

Log loss, also called logistic regression loss or cross-entropy loss, is defined on probability estimates. It is commonly used in (multinomial) logistic regression and neural networks, as well as in some variants of expectation-maximization, and can be used to evaluate the probability outputs ( predict_proba ) of a classifier instead of its discrete predictions.

### Classification and regression - Spark Documentation

Reshape a 7D image into a collection of patches

#### Python - classification: PCA and logistic regression... - Stack Overflow

Predicted class label per sample.

##### Difference Between Classification and Regression (with Comparison...)

Transform a count matrix to a normalized tf or tf-idf representation

###### _— scikit-learn...

The measures precision and recall are popular metrics used to evaluate the quality of a classification system. More recently, receiver operating characteristic (ROC) curves have been used to evaluate the tradeoff between true- and false-positive rates of classification algorithms.

_optics_xi (*, reachability, …)

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.

Partial Dependence Plot (PDP) visualization.

User guide: See the Decomposing signals in components (matrix factorization problems) section for further details.