Explore categorical and numerical features
by clicking on feature names.
Check 'Select's to include them as inputs to your model.
Select a classification model and set parameter values.
Train a model and check model performance metrics.
Proceed to 'Run Scenarios'section to make prediction using the trained model.
Developed by:
mansour.talebizadeh@dataorbs.com
The minimum number of samples required to split an internal node.
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The minimum number of samples required to be at a leaf node.
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The number of features to consider when looking for the best split.
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The seed value used by the random number generator.
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Grow a tree with maximum leaf nodes in best-first fashion (based on relative reduction in impurity).
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The minimum required reduction in impurity for node split.
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The “Balanced” mode assigns weights
inversely proportional to labels frequencies in the training set.
Use "None" for equal class weights.
Fraction of dataset to include in the test split.
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Seed number used for random generation of test dataset.
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Random forest model parameters:
Number of trees in ensemble (forest).
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The criteria for measuring the quality of split.
The maximum depth of the tree.
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The minimum number of samples required to split an internal node.
...
The minimum number of samples required to be at a leaf node.
...
The number of features to consider when looking for the best split.
...
Grow a tree with maximum leaf nodes in best-first fashion (based on relative reduction in impurity).
...
The minimum required reduction in impurity for node split.
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Whether use bootstrap samples or the entire dataset when building trees
Whether to use out-of-bag samples to estimate the generalization accuracy.
Controls both the randomness of the bootstrapping of the samples and the sampling of the
features to consider when looking for the best split at each node.
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When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble,
otherwise, just fit a whole new forest.
The “Balanced” mode assigns weights
inversely proportional to labels frequencies in the training set.
Use "None" for equal class weights.
Fraction of dataset to include in the test split.
...
Seed number used for random generation of test dataset.
...
The AI-enabled dashboard developed by DataOrbs.com provides a unified platform where users
can explore data, select any number of categorical or numeric inputs, train a tree-based
model including a single Decision Tree as well as ensemble of trees (Random Forest),
and make predictions regarding the functioning status of water pumps.