Machinelearning¶
- Multi-Layer Perceptron Classifier
- Decision Tree Classifier
- Random Forest Classifier
- Pipeline decomposition
- Pipeline
- Multi-output classifier
- Multi-output regressor
- Voting Classifier
- Text Count Vectorizer
- Confusion Matrix
- Learning Curve
- ROC from Probabilities
- R² regression score (R2)
- Conditional Probabilty from Categories
- Set Parameters
- Parameter Distribution
- Set Input and Output Names
- Extract Parameters
- Isolation Forest
- Score Cross Validation
- Group K-fold Cross Validation
- Simple Train-Test Split
- K-fold Cross Validation
- Time Series K-fold Based Cross Validation
- Stratified K-fold cross validation
- Leave One Group out Cross Validation
- Split Data for Cross Validation
- Features to Images
- Images to Features
- Example datasets
- Generate classification dataset
- Export Model
- Generate dataset blobs from table
- Import Model
- Generate dataset blobs
- Simulated Annealing Parameter Search
- Randomized Parameter Search
- Grid Parameter Search
- Partial Least Squares cross-decomposition (PLS regression)
- Kernel Principal Component Analysis (KPCA)
- Principal Component Analysis (PCA)
- Extract Attributes
- Standard Scaler
- Label Encoder
- Polynomial Features
- Robust Scaler
- Categorical Encoder
- Normalizer
- One-Hot Encoder
- Label Binarizer
- Binarizer
- Imputer
- Max Abs Scaler
- Support Vector Classifier
- One Class SVM
- Mini-batch K-means Clustering
- K-means Clustering
- Epsilon Support Vector Regression
- Logistic Regression
- Linear Regression
- Kernel Ridge Regression
- Transform Text
- Score
- Fit Texts
- Fit
- Predict Probabilities
- Predict
- Select Features from Model
- Inverse Transform
- Transform
- Fit Transform Text
- Decision Function
- Fit Transform