Achieving Higher Accuracy with Machine Learning In the challenge of classifying raisin types, both Kernel Support Vector Machines (SVMs) and Logistic Regression models initially achieved an accuracy of around 85% during training. But is this good enough? If a human can classify the same raisins with 90% accuracy, there’s clearly room for improvement. Our goal is to enhance the performance of these models on new, unseen data, using the human error rate as our benchmark. Steps to Improve Model Accuracy Train Initial Models: Start by training both Kernel SVMs and Logistic Regression models on your dataset. Analyze Performance: Evaluate the training and validation accuracy of the models to understand their performance. Identify Error Causes: Hypothesize the reasons behind the errors in your models. This could involve examining misclassified data points and identifying patterns or features that might be causing issues. Prioritize Optimization Techniques: Based on the error analysis, decide which optimization techniques to apply first. Common strategies include feature engineering, hyperparameter tuning, and data augmentation. Optimize the Models: Implement the chosen optimization techniques and retrain your models. Iterate: Repeat steps 1 through 5 until you achieve the desired level of accuracy. Select the Best Model: Once all optimizations are complete, identify which model performs best on your test set. Understanding Error Rates Error rates can be categorized into three main phases: Training Phase: This is the performance of the model on the data it was trained on. High training accuracy indicates that the model has learned the training data well, but it may not generalize to new, unseen data. Development (Validation) Phase: This phase assesses the model’s performance on a separate set of data used for tuning hyperparameters. It helps in diagnosing issues such as overfitting or underfitting. Test Phase: The final phase evaluates the model on a completely new dataset to give an impartial measure of its performance. This is crucial for determining how well the model will perform in real-world scenarios. The initial 85% accuracy may seem impressive, but it's important to delve deeper. If humans can achieve 90%, we need to question whether our models are capturing all the relevant features and patterns in the data. By systematically analyzing and addressing the sources of error, we can strive to reach or even surpass the human benchmark. Optimizing SVMs and Logistic Regression Feature Engineering Feature engineering involves creating new input features from existing data to improve model performance. For the raisin classification task, this might include extracting additional features such as shape, color distribution, or texture properties. Enhancing the feature set can provide the model with more information to make accurate predictions. Hyperparameter Tuning Hyperparameters are settings that define how a machine learning model is trained. For Kernel SVMs, key hyperparameters include the choice of kernel function and the regularization parameter ( C ). For Logistic Regression, important hyperparameters include the learning rate and the regularization strength. Grid search or random search methods can be used to find the optimal combination of hyperparameters. Data Augmentation Data augmentation involves increasing the diversity of your training data by applying transformations such as rotation, scaling, and flipping. This can help the model generalize better to new data. For raisin classification, this might mean rotating images of raisins to simulate different angles they might appear in during real-world classification tasks. Applying the Optimization Process Initial Training: Train both Kernel SVMs and Logistic Regression models on the raisin dataset. Record their training and validation accuracies. Error Analysis: Examine the misclassified raisins to identify common characteristics or patterns. This might reveal that certain features are being overlooked or that the data is imbalanced. Hypothesize and Prioritize: Formulate hypotheses about why these errors occur. Prioritize which features to engineer or which hyperparameters to tune based on the insights gained. Implement and Retrain: Apply the selected optimizations and retrain your models. Use cross-validation to ensure that the improvements are robust and not due to chance. Evaluate Performance: Test the models on a held-out test set to evaluate their true performance. If the accuracy is not satisfactory, return to step 3 and continue the process. Conclusion By following a systematic approach to error analysis, feature engineering, and hyperparameter tuning, we can significantly improve the accuracy of our raisin classification models. The human benchmark of 90% serves as a useful reference point, guiding us in our efforts to optimize the models. Ultimately, the goal is to develop a machine learning system that not only matches but potentially exceeds human performance, making the classification task more efficient and reliable.