Understanding the Importance of K Fold Cross Validation in Machine Learning
Introduction:
Machine learning is a complex field that has quickly gained prominence over the past few years. With data growing exponentially, automated decision-making abilities are becoming an essential component for businesses to stay competitive. However, as the data grows, so does the complexity of the machine learning models, making it challenging to achieve both accuracy and robustness. Overcoming this issue requires us to employ sophisticated techniques like K Fold Cross Validation.
What is K Fold Cross Validation?
K Fold Cross Validation is a statistical method that is commonly implemented in machine learning to assess the accuracy and robustness of the predictive model. In simple terms, it is a technique that essentially allows us to obtain a better estimate of the model’s performance by splitting the dataset into K-subsets.
How K Fold Cross Validation works:
The first step in executing K Fold Cross Validation is splitting the dataset into K-subsets. Once the data has been partitioned, the model needs to be trained on K-1 folds and then validated on the remaining fold. This process continues K times, with each of the K-subsets being used as a validation set precisely once.
Benefits of K Fold Cross Validation:
1. Avoids Overfitting: K Fold Cross Validation helps to determine the generalizability of the training dataset. This technique mitigates the effect of overfitting and helps to avoid poor performance of the model on independent datasets.
2. More Accurate: When working with a limited dataset, K Fold Cross Validation allows for the better utilization of data. By training and validating the model over multiple folds, and then averaging the accuracy measures, one can get more realistic estimates about the performance of the model.
Real-Life Examples:
An excellent example of how K Fold Cross Validation can be used lies in the identification of handwritten digits from a dataset of images. By using K Fold Cross Validation, the model accuracy increased from 70% to 80%.
Another example of K Fold Cross Validation in action is in the detection of lung cancer using Convolutional Neural Networks (CNN). The CNN model was evaluated using K Fold Cross Validation with K=5, resulting in an average accuracy of 71.5%.
Conclusion:
K Fold Cross Validation is a crucial methodology that ensures the model’s accuracy and robustness in machine learning. By implementing K Fold Cross Validation, one can avoid overfitting and get more reliable estimates of the model. The Real-life examples also demonstrate the effectiveness of the technique in enhancing the model’s performance. Therefore, when building a machine learning model, K Fold Cross Validation should always be considered for better performance and more accurate results.
