In this repositery Decision Tree model is implemented in python using the ML scikit-learn package. DT's are also visualized using the graphviz package. The Datasets(training & testing) used for making decision tree classifer are taken from UCI Machine Learning Repository: archive.ics.uci.edu/ml/datasets/Forest+type+mapping
Decision Tree Classifiers helps to classify and divide raw data into classified data (i.e. in particular types).
4 steps for making Decision Tree Classifier
- Import Datasets (By pandas package)
- Train a classifier (By training set)
- Predict label for new flower given its attributes (By testing set)
- Visualize the tree (By scikit learn package)