https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/Recent content in Spark ML Models onHugo -- gohugo.ioSun, 11 Dec 2022 19:37:55 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/decision_tree_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/decision_tree_algorithm/Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees algorithms are easy to interpret, they handle categorical features, extend to the multi-class classification setting, do not require feature scaling, and are able to capture non-linearity and feature interactions. Decision Tree Analytics processor is used to analyze data using ML’s DecisionTreeClassificationModel and DecisionTreeRegressionModel. To use a Decision Tree Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right click on it to configure:https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/gradient_boosted_tree_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/gradient_boosted_tree_algorithm/Gradient-Boosted Trees (GBTs) are ensembles of decision trees. GBTs can be used for binary classification and for regression, using both continuous and categorical features. Gradient-Boosted Trees Analytics processor is used to analyze data using ML’s GBTClassificationModel and GBTRegressionModel. 👉 GBTs does not support multi-label classification. To use a GBT Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right click on it to configure.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/isotonic_regression_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/isotonic_regression_algorithm/Isotonic Regression belongs to the family of Regression algorithms. It gives an approximate series of one-dimensional observations with a non-decreasing function. Isotonic Regression Analytics processor analyzes data using ML Isotonic Regression Model. To use an Isotonic Regression Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right-click on it to configure. The Configuration Section → of every ML model is identical. After the Configuration tab comes the Feature Selection → tab.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/k-means_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/k-means_algorithm/K-Means is one of the most commonly used clustering algorithms that clusters the data points into a predefined number of clusters. K-Means Analytics processor is used to analyze data using ML’s K-means Model. To use a K-Means Model in Data Pipeline, drag and drop the model component to the pipeline and right click on it to configure. The Configuration Section → of every ML model is identical. After the Configuration tab comes the Feature Selection → tab.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/linear_regression_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/linear_regression_algorithm/Regression is an approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables (or independent variables). Regression Analytics processor is used to analyze data using ML LinearRegressionModel. To use a Linear Regression Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right click on it to configure. The Configuration Section → of every ML model is identical. After the Configuration tab comes the Feature Selection → tab.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/logistic_regression_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/logistic_regression_algorithm/Logistic regression is a popular method to predict a categorical response. A special case of generalized linear models predicts the probability of the outcomes. It can be used for both binary and multi-class classification problems. Logistic Regression Analytics processor is used to analyze data using ML’s Logistic Regression Model. To us e a Logistic Regression Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right-click on it to configure.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/naive_bayes_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/naive_bayes_algorithm/Naive Bayes are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. Currently, it supports both multinomial Naive Bayes and Bernoulli Naive Bayes. Naive Bayes Analytics processor is used to analyze data using ML’s NaiveBayesModel. To use a Naïve Bayes Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right-click on it to configure.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/random_forest_trees_algorithm/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/spark-ml-models/random_forest_trees_algorithm/Random forests are ensembles of decision trees. Random forests combine many decision trees to reduce the risk of over fitting. Random forests can be used for binary and multi-class classification and for regression, using both continuous and categorical features. Random Forest Trees Analytics processor is used to analyze data using ML’s RandomForestClassificationModel and RandomForestRegressionModel. To use a Random Forest Trees Regression Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right click on it to configure.