klasifikasi diabetess

Use this node to read CSV files into your workflow. The node will produce a data table with numbers and types of columns guessed automatically.

Converts numbers in a column (or a set of columns) to strings. Note that for an advanced configuration, such as rounding or representation in scientific notification you can also use the "Round Double" node.

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

Learns a random forest*, which consists of a chosen number of decision trees. Each of the decision tree models is built with a different set of rows (records) and for each split within a tree a randomly chosen set of columns (describing attributes) i

Predicts patterns by aggregating the predictions of the individual trees in a random forest model.Random Forests is a registered trademark of Minitab, LLC and is used with Minitab’s permission.

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of a given attribute and its classification match. The dialog allows you to select two columns for comparison; the values from the first selected c

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of a given attribute and its classification match. The dialog allows you to select two columns for comparison; the values from the first selected c

Performs a multinomial logistic regression. Select in the dialog a target column (combo box on top), i.e. the response. The solver combo box allows you to select which solver should be used for the problem (see below for details on the different solv

Classifies data using a Gradient Boosted Trees model.The implementation follows the algorithms described in Greedy Function Approximation: A Gradient Boosting Machine by Jerome H. Friedman (1999).

Predicts the response using a logistic regression model. The node needs to be connected to a logistic regression node model and some test data. It is only executable if the test data contains the columns that are used by the learner model.

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of a given attribute and its classification match. The dialog allows you to select two columns for comparison; the values from the first selected c

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

Learns Gradient Boosted Trees with the objective of classification. The algorithm uses very shallow regression trees and a special form of boosting to build an ensemble of trees. The implementation follows the algorithm in section 4.6 of the paper "G