| Year |
2024 |
| Volume/Issue/Review Month |
Volume 1 | Special Issue | October |
| Title |
Cross-validation of Machine Learning approach with implementation of Random Forest Classifier Model using Python |
| Authors |
Subhasis Mohapatra, Aloka Natha, Nishanta Ranjan Nanda |
| Broad area |
Cross-validation of Machine Learning approach with implementation of Random Forest Classifier Model using Python |
| Abstract |
This paper presents a comprehensive exploration of the cross-validation technique in
the context of machine learning, with a focus on the implementation of the Random Forest Classifier
model using Python. Cross-validation is a crucial method for assessing the robustness and general
ability of a machine-learning model. It involves partitioning the data into subsets, training the
model on these subsets, and then testing it on the complementary subset to validate the model’s
predictions. This study aims to serve as a valuable resource for researchers and practitioners in
the field of machine learning, offering detailed guidance on implementing robust machine-learning
models using cross-validation techniques and Python programming. The need for cross validating
machine learning models is extremely important as we implement it for the problem-solving purpose.
Usually in data science assumption is to go through various models to find a better ML model.
However, it becomes difficult to find distinction whether this improvement in score is visible because
we are capturing the relationship in better approach or we are just over fitting the input data. This
model helps us to achieve more generalized relationships and find suitable model for the problem
solving. Experimental results underscore the effectiveness of the Random Forest Classifier in various
scenarios, providing insights into its performance across different cross-validation schemes. The
analysis helps in understanding how cross-validation can be used to fine-tune model parameters
and prevent issues like overfitting, thereby enhancing the predictive accuracy of the model. |
| File |
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| Referenceses |
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