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Predictive Analytics (with R)

Input File (.csv only):

Condition(s) for Subset:
Categorical Variable
Categorical Variable
Categorical Variable
Categorical Variable

Dependent Variable:
Change to Categorical Variable

Independent Variable(s):



From Top From Bottom


% of Training Data

Include Prediction for Validation Data with Testing Data


Linear Regression
Decision Tree (with Levels)
Random Forest with trees with maximum nodes
Gradient Boosting
Extreme Gradient Boosting (XGBoost) (with maximum depth = )
Support Vector Machine (with kernel = )


Linear Discriminant Analysis
Logistic Regression for binary classification with cut-off =
Decision Tree (with Levels)
Random Forest with trees with maximum nodes
Naive Bayes
Support Vector Machine (with kernel = )
Neural Network (with hidden layers)
Adaptive Boosting (AdaBoost)
Gradient Boosting for binary classification with cut-off =
Extreme Gradient Boosting (XGBoost)) for binary classification with cut-off = and maximum depth =
k-Nearest Neighbor (with k = ) (Note: numeric independent variables only)


Mean Squared Error (MSE)
Mean Absolute Error (MAE)


Precision
Recall or Sensitivity
Specificity
F1 Score








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