Out of the Box: Machine Learning

Part of the Talend (Real-Time) Big Data Platform is a large assortment of Machine Learning components which allow for analysis to be performed directly in the Talend Studio without custom coding.

The full list, description, and related documentation for Machine Learning components is detailed in the table below. For complete information on the Machine Learning components, see the documentation.

Icon	Name	Component	Description
	ALS (Alternating Least Squares)	tALSModel	Process information received from Spark and performs ALS computations over these sets to generate and write a product recommender model (user ranking) in Parquet format
	Bayes (Naïve)	tNaiveBayesModel	Analyzes data sets and applies Bayes’ law with a naïve assumption and generates a classification model in PMML (Predictive Model Markup Language) format
	Classification	tClassify	Uses a given model to classify elements in the dataset
	Classification (Support Vector Machine)	tClassifySVM	Uses SVM (Support Vector Machines) model to classify elements in the dataset
	Decision Trees	tDecisionTreeModel	Uses the Decision Tree algorithm to generate a classification model
	Gradient Boosted Tree Model	tGradientBoostedTreeModel	Generates a binary classification model
	K-Means	tKMeansModel	Analyzes incoming datasets and applies K-means algorithm producing a clustering model
	K-Means (Streaming)	tKMeansStrModel	Analyzes incoming datasets and applies K-means algorithm in real-time
	Linear Regression	tLinearRegressionModel	Builds a linear regression model using a training dataset
	Logistic Regression	tLogisticRegressionModel	Analyzes incoming datasets and applies Logistic Regression algorithm producing a classification model
	Model Encoder	tModelEncoder	Can apply a wide range of feature processing algorithms: HashingTF, Inverse document frequency, Word2Vector, CountVectorizer, Binarizer, Bucketizer, Discrete Cosine Transform (DCT), MinMaxScaler, N-gram, Normalizer, One hot enconder, PCA, Polynomial expansion, Quantile Discretizer, Regex tokenizer, Tokenizer, SQL Transformer, Standard scaler, StopWordsRemover, String indexer, Vector indexer, Vector assembler, ChiSQSelector, RFormula, VectorSlicer
	Predict	tPredict	Uses a given classification, clustering or relationship model to analyse datasets
	Predict (Cluster)	tPredictCluster	Uses a given clustering model to analyse datasets into different clusters
	Random Forest Model	tRandomForestModel	Analyzes incoming datasets and applies Random Forest algorithm
	Recommend	tRecommend	Analyzes incoming data in conjunction with ALS computations using a user defined recommendation model
	SVM (Support Vector Machine)	tSVMModel	Applies SVM algorithm to analyze feature vectors