Random Forest Algorithm: A Machine Learning ALgorithm.pdf
- 1. RANDOM FOREST ALGORITHM - SHALINI, REEENA & SUHAANA
- 2. CONTENTS 01 MachineLearning and its Types 04 DecisionTree 02 Random Forest Algorithm 03 05 Why Random Forest? Applicationsof Random Forest
- 3. Machine Learning MachineLearning ● Machine learning is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience. ● Instead of being explicitly programmed for a task, machines learn from data and make predictions or decisions based on patterns and relationships discovered in that data.
- 4. Types of machine learning Types of Machine Learning Mobile banking UnsupervisedLearning ReinforcementLearning SupervisedLearning
- 5. Decision Tree DecisionTree ● A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. ● The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization. ● Decision trees imitate human thinking, so it’s generally easy for data scientists to understand and interpret the results.
- 9. Random Forest ● A Random Forest Algorithm is a supervised machine learning algorithm that is extremely popular and is used for Classification and Regression problems in Machine Learning. ● The “forest” it builds is an ensemble of decision trees, usually trained with the bagging method. The general idea of the bagging method is that a combination of learning models increasesthe overall result. ● Random Forest builds multiple decision trees and merges them together to get a more accurate and stable prediction.
- 10. Working of Random Forest Algorithm 01 Selectrandomsamples froma given data ortraining set. 02 This algorithmwill constructa decision tree forevery training data. 03 Voting will take place byaveraging the decision tree. 04 Finally, selectthe mostvotedprediction resultas the final prediction result.
- 11. Working of Random Forest Algorithm
- 12. Random Forest Example CONDITION Colour == Red ? Diameter == 3 Colour == Orange? Diameter == 1 TRAINING DATASET COLOR DIAMETER LABEL Red 3 Apple Red 1 Cherry Red 3 Apple Orange 3 Orange Red 1 Cherry
- 13. Random Forest Example Determinethenameof the fruit ?
- 16. Why Random Forest ?
- 17. Applications of Random Forest 04 Healthcare 03 Customer Churn Prediction 02 Image and Speech Recognition 01 Anomaly Detection Applicationsof Random Forest
- 18. Thank You