Decision Tree
A decision tree can be used to visually and explicitly represent decisions and decision making. As the name goes, it uses a tree-like model of decisions that have influenced a wide area of machine learning, covering both classification and regression.
- If Dt contains records that belong the same class yt, then t is a leaf node labeled as yt.
- If Dt is an empty set, then t is a leaf node labeled by the default class, yd.
- If Dt contains records that belong to more than one class, use an attribute test to split the data into smaller subsets.
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GINI IMPURITY
Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. -
ENTROPY
Entropy is the measure of randomness in the universe of discourse.
Source: https://en.wikipedia.org/wiki/Decision_tree_learning
- Generally Data is split into Training data and Testing data for model training.
- Model is trained on Training Data and later on, its accuracy is calculated on the basis of Results taken using Testing Dataset.
- While training the model a very common complication occurs that is called OVER-fitting.
- Basically, overfitting refers to a model which has learned the training data so well, that it fails to work accurately on Validation Set.
- In actuality, the model learns the noise and details of training data, hence, it negatively impacts the performance of the model on new (test) data.
- A Decision Tree is Pruned to reduce overfitting and attain better results.
- Pruning reduces the size of decision trees by removing parts of the tree that do not provide power to classify instances.
- At each stage of splitting the tree, we check the cross-validation error. If the error does not decrease significantly enough then we stop.