23.6 Designer: Optimizing Model Fit

Cross-Validation

Overfitting is a common problem in ML modeling. Let's fix that problem with cross-validation. Follow along with Microsoft ML Studio: Cross Validate Model to learn the Cross Validate Model pillDivides dataset into "folds" and evaluates each fold against the testing data. Scores the entire dataset and is a better alternative to the more traditional "Split Data" technique although it's more computationally intensive.. See the complete documentation here: https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/cross-validate-model?view=azureml-api-2

This video uses the bikebuyers.csvNot Found dataset:

Concepts

In the video, I talked about two ways to use k-fold cross-validation. First, the basic form of cross-validation is the image below:

Second, k-fold cross-validation can be used in conjunction together with a train/test split where the cross-validation is performed on the training data while the testing data is reserved for a final evaluation. As implemented in AMLS Designer, this second option doesn't appear to be a viable option. However, it is often used in practice when building ML pipelines from code in Python or R programming languages. So I wanted you to still understand the concept.

Summary

Cross-validation is an important technique in machine learning used to evaluate the performance of a model in a more reliable manner. It involves partitioning the dataset into multiple subsets, training the model on some subsets, and validating it on the remaining subsets. Here is a detailed description of the cross-validation process, including its advantages and disadvantages.

• Steps in cross-validation

1. Data partitioning: Divide the dataset into k subsets (or folds). Common choices for k are 5 or 10, but this can vary depending on the size of the dataset and the specific requirements of the analysis.

2. Training and validation:

• Iteration over Folds: For each fold, treat it as the validation set, and use the remaining k-1 folds as the training set.

• Model Training: Train the model on the k-1 training folds.

• Model Evaluation: Validate the trained model on the remaining validation fold and record the performance metric (e.g., accuracy, F1 score, mean squared error).

3. Repeat the Process: Repeat the training and validation process for each of the k folds, ensuring that each fold serves as the validation set exactly once.

4. Aggregate Results: Calculate the average and standard deviation of the performance metrics across all k folds. This provides a more reliable estimate of the model's performance.

• Variants of cross-validation

1. k-Fold Cross-Validation: The dataset is divided into k equal-sized folds. Each fold is used as the validation set exactly once. This is the version used by the AMLS Designer pill for cross-validation.

2. Stratified k-Fold Cross-Validation: Similar to k-fold but ensures that each fold has approximately the same distribution of class labels, which is particularly useful for imbalanced datasets.

3. Leave-One-Out Cross-Validation (LOOCV): A special case of k-fold where k equals the number of data points in the dataset. Each fold consists of a single data point, making this method computationally expensive but useful for small datasets.

4. Time Series Cross-Validation: Specifically designed for time series data, where the order of data points matters. It typically involves training on past data and validating on future data to preserve temporal order.

• Advantages of cross-validation

1. More Reliable Estimates: Provides a more robust and reliable estimate of model performance compared to a single train-test split, as it evaluates the model on multiple subsets of the data.

2. Reduces Overfitting: By validating the model on multiple folds, it reduces the risk of overfitting to a particular train-test split.

3. Efficient Use of Data: Makes efficient use of the available data by using all data points for both training and validation across different iterations.

4. Hyperparameter Tuning: Facilitates the tuning of hyperparameters by providing a reliable performance metric to optimize against.

• Disadvantages of cross-validation

1. Computationally Expensive: Requires training and validating the model multiple times (k times for k-fold cross-validation), which can be computationally intensive, especially for large datasets and complex models.

2. Time-Consuming: The iterative process of training and validating the model can be time-consuming, particularly for large datasets.

3. Complexity in Implementation: Implementing cross-validation, especially with custom data processing pipelines, can add complexity to the model training workflow.

Cross-validation is a very useful method for evaluating the performance of machine learning models. It provides a more reliable and robust estimate than a simple train-test split. While it offers significant advantages in terms of reducing overfitting and making efficient use of data, it also comes with challenges such as increased computational cost and complexity. Balancing these factors is crucial for effectively leveraging cross-validation in machine learning pipelines.

Hyperparameter Tuning

So far, you have learned how to try out various statistical algorithms to find out which one is best. Now, let's learn how to squeeze every last drop of excellence out of your data. To do this, you are going to learn the Tune Model Hyperparameters pillDynamically chooses the best algorithm "hyper"-parameters to maximize model effectiveness.. Follow along with the video below to see how this works.

This video uses the bikebuyers.csvNot Found dataset:

Summary

Okay, that one pill took a long time to run. That's because it was doing a lot of stuff under the hood. Let's review some of the main concepts from the video and add a few more. Tuning the hyperparameters of machine learning algorithms involves adjusting the parameters that govern the training process of the algorithm. These parameters are not learned from the data but are set before the learning process begins. The goal of hyperparameter tuning is to find the optimal set of hyperparameters that maximize the performance of the model.

• Steps of hyperparameter tuning (this is a general set of steps that are tool-agnostic):

1. Define the hyperparameters and their range:

• Identify the hyperparameters to tune (e.g., learning rate, number of layers, batch size). This is based on the algorithm selected (e.g. linear/logistic regression, boosted decision trees, decision forest, neural network, support vector machines, etc).

• Specify the range of values for each hyperparameter.

2. Choose a Search Strategy:

• Grid Search: Exhaustively searches through a specified subset of the hyperparameter space. It evaluates all possible combinations.

• Random Search: Randomly samples the hyperparameter space. It is often more efficient than grid search.

• Bayesian Optimization: Uses a probabilistic model to find the hyperparameters. It balances exploration and exploitation to find the optimal set.

• Gradient-based Optimization: Uses gradients to optimize hyperparameters, typically applicable to differentiable hyperparameters.

• Genetic Algorithms: Uses evolutionary strategies to explore the hyperparameter space.

3. Cross-validation:

• Split the training data into k-folds.

• For each combination of hyperparameters, train the model on k − 1 folds and validate it on the remaining fold.

• Repeat for all folds and compute the average performance metric.

4. Evaluate and select the best model:

• Compare the performance metrics (e.g., accuracy, F1 score) of all models.

• Select the set of hyperparameters that yield the best performance on the validation set.

5. Train the final model:

• Train the final model using the best hyperparameters on the entire training dataset.

• Validate the final model on a separate test dataset to ensure generalization.

• Advantages of hyperparameter tuning

1. Improved Performance: Proper tuning can significantly enhance model accuracy and generalization.

2. Model Robustness: Fine-tuning can make the model more robust to overfitting and underfitting.

3. Optimization of Resources: Efficient search strategies like Bayesian optimization can save computational resources and time.

4. Adaptability: Hyperparameter tuning makes it easier to adapt models to different datasets and tasks.

• Disadvantages of hyperparameter tuning

1. Computationally Intensive: Exhaustive methods like grid search can be computationally expensive and time-consuming, especially for large datasets and complex models.

2. Risk of Overfitting: Excessive tuning on the validation set can lead to overfitting, where the model performs well on validation data but poorly on unseen test data.

3. Complexity: The process can be complex and require expertise to set appropriate ranges and choose the right search strategy.

4. Resource Constraints: Limited computational resources can restrict the extent of hyperparameter tuning, potentially leading to suboptimal models.

5. Diminishing Returns: After a certain point, further tuning may yield minimal improvements, making it important to balance the effort invested in tuning versus the gains achieved.

Does the Tune Hyperparameters pill in AMLS Designer really do all of that? Yes it does. When it comes to the search strategy, AMLS Designer offers to options: Entire grid ("Grid Search" above) and random sweep. But as you can see, there are some other options available if you are willing the write the code yourself. If you want to see the pill's documentation in detail, you can find it here: https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/tune-model-hyperparameters?view=azureml-api-2

The other point above that you may have questions about is cross-validation. That is an important topic and the subject of the next section. So hang tight for a moment and we'll get into that next.

In closing, hyperparameter tuning is a crucial step in the machine learning pipeline to optimize model performance. While it offers significant benefits in terms of model accuracy and robustness, it also comes with challenges such as computational cost and complexity. Choosing the right tuning strategy and balancing resource constraints is essential for effective hyperparameter optimization.

Assessment

Complete the assessment below: