# How to choose an ML.NET algorithm

For each ML.NET task, there are multiple training algorithms to choose from. Which one to choose depends on the problem you are trying to solve, the characteristics of your data, and the compute and storage resources you have available. It is important to note that training a machine learning model is an iterative process. You might need to try multiple algorithms to find the one that works best.

Algorithms operate on features. Features are numerical values computed from your input data. They are optimal inputs for machine learning algorithms. You transform your raw input data into features using one or more data transforms. For example, text data is transformed into a set of word counts and word combination counts. Once the features have been extracted from a raw data type using data transforms, they are referred to as featurized. For example, featurized text, or featurized image data.

## Trainer = Algorithm + Task

An algorithm is the math that executes to produce a model. Different algorithms produce models with different characteristics.

With ML.NET, the same algorithm can be applied to different tasks. For example, Stochastic Dual Coordinate Ascent can be used for Binary Classification, Multiclass Classification, and Regression. The difference is in how the output of the algorithm is interpreted to match the task.

For each algorithm/task combination, ML.NET provides a component that executes the training algorithm and makes the interpretation. These components are called trainers. For example, the SdcaRegressionTrainer uses the StochasticDualCoordinatedAscent algorithm applied to the Regression task.

## Linear algorithms

Linear algorithms produce a model that calculates scores from a linear combination of the input data and a set of weights. The weights are parameters of the model estimated during training.

Linear algorithms work well for features that are linearly separable.

Before training with a linear algorithm, the features should be normalized. This prevents one feature from having more influence over the result than others.

In general, linear algorithms are scalable, fast, cheap to train, and cheap to predict. They scale by the number of features and approximately by the size of the training data set.

Linear algorithms make multiple passes over the training data. If your dataset fits into memory, then adding a cache checkpoint to your ML.NET pipeline before appending the trainer will make the training run faster.

### Averaged perceptron

Best for text classification.

AveragedPerceptronTrainer Binary classification Yes

### Stochastic dual coordinated ascent

Tuning not needed for good default performance.

SdcaLogisticRegressionBinaryTrainer Binary classification Yes
SdcaNonCalibratedBinaryTrainer Binary classification Yes
SdcaMaximumEntropyMulticlassTrainer Multiclass classification Yes
SdcaNonCalibratedMulticlassTrainer Multiclass classification Yes
SdcaRegressionTrainer Regression Yes

### L-BFGS

Use when number of features is large. Produces logistic regression training statistics, but doesn't scale as well as the AveragedPerceptronTrainer.

LbfgsLogisticRegressionBinaryTrainer Binary classification Yes
LbfgsMaximumEntropyMulticlassTrainer Multiclass classification Yes
LbfgsPoissonRegressionTrainer Regression Yes

Fastest and most accurate linear binary classification trainer. Scales well with number of processors.

SymbolicSgdLogisticRegressionBinaryTrainer Binary classification Yes

Implements the standard (non-batch) stochastic gradient descent, with a choice of loss functions, and an option to update the weight vector using the average of the vectors seen over time.

## Decision tree algorithms

Decision tree algorithms create a model that contains a series of decisions: effectively a flow chart through the data values.

Features do not need to be linearly separable to use this type of algorithm. And features do not need to be normalized, because the individual values in the feature vector are used independently in the decision process.

Decision tree algorithms are generally very accurate.

Except for Generalized Additive Models (GAMs), tree models can lack explainability when the number of features is large.

Decision tree algorithms take more resources and do not scale as well as linear ones do. They do perform well on datasets that can fit into memory.

Boosted decision trees are an ensemble of small trees where each tree scores the input data and passes the score onto the next tree to produce a better score, and so on, where each tree in the ensemble improves on the previous.

Fastest and most accurate of the binary classification tree trainers. Highly tunable.

LightGbmBinaryTrainer Binary classification Yes
LightGbmMulticlassTrainer Multiclass classification Yes
LightGbmRegressionTrainer Regression Yes
LightGbmRankingTrainer Ranking No

### Fast tree

Use for featurized image data. Resilient to unbalanced data. Highly tunable.

FastTreeBinaryTrainer Binary classification Yes
FastTreeRegressionTrainer Regression Yes
FastTreeTweedieTrainer Regression Yes
FastTreeRankingTrainer Ranking No

### Fast forest

Works well with noisy data.

FastForestBinaryTrainer Binary classification Yes
FastForestRegressionTrainer Regression Yes

Best for problems that perform well with tree algorithms but where explainability is a priority.

GamBinaryTrainer Binary classification No
GamRegressionTrainer Regression No

## Matrix factorization

### Matrix Factorization

Used for collaborative filtering in recommendation.

MatrixFactorizationTrainer Recommendation No

### Field Aware Factorization Machine

Best for sparse categorical data, with large datasets.

FieldAwareFactorizationMachineTrainer Binary classification No

## Meta algorithms

These trainers create a multiclass trainer from a binary trainer. Use with AveragedPerceptronTrainer, LbfgsLogisticRegressionBinaryTrainer, SymbolicSgdLogisticRegressionBinaryTrainer, LightGbmBinaryTrainer, FastTreeBinaryTrainer, FastForestBinaryTrainer, GamBinaryTrainer.

### One versus all

This multiclass classifier trains one binary classifier for each class, which distinguishes that class from all other classes. Is limited in scale by the number of classes to categorize.

OneVersusAllTrainer Multiclass classification Yes

### Pairwise coupling

This multiclass classifier trains a binary classification algorithm on each pair of classes. Is limited in scale by the number of classes, as each combination of two classes must be trained.

PairwiseCouplingTrainer Multiclass classification No

## K-Means

Used for clustering.

KMeansTrainer Clustering Yes

## Principal component analysis

Used for anomaly detection.

RandomizedPcaTrainer Anomaly detection No

## Naive Bayes

Use this multi-class classification algorithm when the features are independent, and the training dataset is small.

NaiveBayesMulticlassTrainer Multiclass classification Yes

## Prior Trainer

Use this binary classification algorithm to baseline the performance of other trainers. To be effective, the metrics of the other trainers should be better than the prior trainer.

PriorTrainer Binary classification Yes

## Support vector machines

Support vector machines (SVMs) are an extremely popular and well-researched class of supervised learning models, which can be used in linear and non-linear classification tasks.

Recent research has focused on ways to optimize these models to efficiently scale to larger training sets.

### Linear SVM

Predicts a target using a linear binary classification model trained over boolean labeled data. Alternates between stochastic gradient descent steps and projection steps.

LinearSvmTrainer Binary classification Yes

### Local Deep SVM

Predicts a target using a non-linear binary classification model. Reduces the prediction time cost; the prediction cost grows logarithmically with the size of the training set, rather than linearly, with a tolerable loss in classification accuracy.