Icon

09. Advanced Data Mining - solution

Advanced Data Mining - Solution
Activity I: Random Forest Model - Read CurrentDetailData.table data - Partition the data 50/50 using stratified sampling on the "Target" column - Create a Random Forest model to predict the "Target" column - Use a tree depth of 5 and 50 models Activity II: Parameter Optimization - Add a parameter optimization loop to your model training process - Use Hillclimbing to determine the optimum number of models (min=10, max=200, step=10, int = yes) - Use maximum accuracy as the objective value - What is the best number of models?(Hint: don't forget to use the flow variable in the Random Forest Learner node) Activity III: Cross Validation - Create a 10-fold cross validation for your model - Take a look at the error rates produced by the different iterations. Does the model seem stable? Define ParametersCollect Accuracy Table Reader X-Partitioner Partitioning Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End X-Aggregator Partitioning Scorer Random ForestLearner Random ForestPredictor Random ForestLearner Random ForestPredictor Random ForestLearner Random ForestPredictor Activity I: Random Forest Model - Read CurrentDetailData.table data - Partition the data 50/50 using stratified sampling on the "Target" column - Create a Random Forest model to predict the "Target" column - Use a tree depth of 5 and 50 models Activity II: Parameter Optimization - Add a parameter optimization loop to your model training process - Use Hillclimbing to determine the optimum number of models (min=10, max=200, step=10, int = yes) - Use maximum accuracy as the objective value - What is the best number of models?(Hint: don't forget to use the flow variable in the Random Forest Learner node) Activity III: Cross Validation - Create a 10-fold cross validation for your model - Take a look at the error rates produced by the different iterations. Does the model seem stable? Define ParametersCollect Accuracy Table Reader X-Partitioner Partitioning Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End X-Aggregator Partitioning Scorer Random ForestLearner Random ForestPredictor Random ForestLearner Random ForestPredictor Random ForestLearner Random ForestPredictor

Nodes

Extensions

Links