TECH REPORT

SIPNA COLLEGE OF ENGINEERING AND TECHNOLOGY, AMRAVATIDepartment of Information Technology

Academic Year : 2023-2024 Semester : Seventh

Presentation on : "RANDOM FOREST ALGORITHM IN PREDICTIVE ANALYSIS"

Presented By : Siddhi Adhiya Guided By : Prof. G. D. Govindwar

START

Predictive Analysis

RFA

INDEX

Origin of RFA

Why RFA is used in Predictive Analysis

Working with Example

Advantages & Disadvantages

01

Predictive Analysis

RFA in Predictive Analysis

• Predictive analysis focuses on creating future predictions from data.

• It uses a variety of statistical models, techniques, and tools that all aid in understanding the patterns in datasets and making predictions.

• An example of a predictive Analysis is disease prediction.

DESCRIBE

01

DIAGNOSE

02

PREDICT

03

PRESCRIBE

04

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02

Random Forest Algorithm

RFA in Predictive Analysis

• The random forest algorithm is used for both classification and regression tasks.

• The random forest algorithm constructs a collection of decision trees and aggregates their outputs to generate the final prediction.

• Random Forest calculates the accuray and shows the importance of variables.

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02

Random Forest Algorithm

RFA in Predictive Analysis

Flow chart

Step 1: In the Random forest model, a subset of data points and a subset of features is selected for constructing each decision tree. Simply put, n random records and m features are taken from the data set having k number of records. Step 2: Individual decision trees are constructed for each sample. Step 3: Each decision tree will generate an output. Step 4: Final output is considered based on Majority Voting or Averaging for Classification and regression, respectively.

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03

Origin of RFA

RFA in Predictive Analysis

• Random forest is a commonly-used machine learning algorithm trademarked by Leo Breiman and Adele Cutler, which combines the output of multiple decision trees to reach a single result.
• The random forest algorithm was created to address some of the limitations of individual decision trees, such as high variance and overfitting.

• The key motivations behind developing the random forest algorithm were:

Reduction of Variance

Handling high dimensional data

Mitigation of overfitting

• Reduce this variance by combining predictions from multiple decision trees.

• Random forest can handle datasets with a large number of input features, making it suitable for high-dimensional data.

• Randomness helps to decorrelate the trees and reduces the risk of overfitting.

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04

Why RFA is used in Predictive Analysis

RFA in Predictive Analysis

Handling High-Dimensional Data

Accuracy

Robustness

• Mitigate the impact of individual noisy or outlier predictions, resulting in more robust predictions.

• Capture relevant patterns and reduce the impact of irrelevant features.
• Predictive systems dealing with complex datasets.

• Reduce overfitting and variance
• robust and accurate predictions.

Handling Imbalanced Data

Interpretability

Parallelizability

• Understanding which features contribute the most to predictions can help interpret the results of the predictive system.

• Voting mechanism and the construction of multiple decision trees provide robustness against class imbalance.

• Multi-core processors or distributed computing frameworks.

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05

Working of Algorithm (Example)

RFA in Predictive Analysis

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06

Advantages and Disadvantages

RFA in Predictive Analysis

Advantages

Random Forest can automatically handle missing values.

Handles both categorical and continuous variables.

Handles non-linear parameters efficiently

Disadvantages

Complexity

Longer Training Period

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Applications

RFA in Predictive Analysis

Stock Market

Healthcare

Fianance

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Conclusion

RFA in Predictive Analysis

• Overall, the Random Forest algorithm's combination of accuracy, robustness, handling of high-dimensional data, and feature importance estimation make it a powerful tool in predictive systems.

• Its applications span various domains, including healthcare, finance, marketing, and image recognition.

• By leveraging the strengths of ensemble learning, Random Forest contributes to more reliable predictions and data-driven decision-making in predictive systems

• The algorithm's voting mechanism and construction of multiple decision trees provide robustness against class imbalance.

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Thank You!

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