Holdout method:
All data is randomly divided into same equal size data sets. e.g,
- Training set
- Test set
- Validation set
Training set:
- It is a data set helps in the prediction of the model.
[quads id=1]
Test set:
- Unseen data is used as a subset of the data set to assess the performance of the model.
Validation set:
- The validation set is also a data set used to assess the performance of model built during the training.
For example;
There are total 3 data sets.
Total training set for model construction
- 2/3
Total test set for accuracy estimation
- 1/3
Next Similar Tutorials
- Decision tree induction on categorical attributes – Click Here
- Decision Tree Induction and Entropy in data mining – Click Here
- Overfitting of decision tree and tree pruning – Click Here
- Attribute selection Measures – Click Here
- Computing Information-Gain for Continuous-Valued Attributes in data mining – Click Here
- Gini index for binary variables – Click Here
- Bagging and Bootstrap in Data Mining, Machine Learning – Click Here
- Evaluation of a classifier by confusion matrix in data mining – Click Here
- Holdout method for evaluating a classifier in data mining – Click Here
- RainForest Algorithm / Framework – Click Here
- Boosting in data mining – Click Here
- Naive Bayes Classifier – Click Here