How to Train a Machine Learning Model in the USA

1. Data Set;

The data set we will use was collected from our machine learning project using the Google Colab platform. To ensure that the model is generalizable, we will limit the training dataset to only the last 70 percent of the data. To train the model, we will use random under-sampling (RUS) to build our initial predictive model. RUS helps us reduce overfitting while ensuring reliable results. We will then fine-tune our model by applying dropout regularization, and increasing the number of training iterations. Finally, our model will be validated by predicting the remaining 30% of test data.

How to Train a Machine Learning Model in the USA

2. Models;

Our first model will be based on logistic regression. Logistic regression is helpful when there is a binary outcome of interest. A binary outcome refers to two categories of data: 1 for positive cases, and 0 otherwise. We have a total of three outcomes we need to predict: 1) whether a student passed the test 2) whether a student dropped out of school, and 3) whether a student graduated high school. We will use these outcomes to create three different models: logistic regression, k-nearest neighbors, and decision trees.

3. Hyperparameters;

We will use hyperparameter tuning to find the best values for our model’s hyperparameters. We will start with the standard tuning parameters: number of training iterations, dropout probability, and batch size. Then, we will look at the following tuning options: dimensionality reduction methods (PCA/LDA), the type of feature selection method (filter vs wrapper), and the number of features to consider. All of these parameters are tuned independently, so we can find their optimal value without any bias toward one parameter over another.

4. Model Training;

To perform hyperparameter tuning, we will use grid search with the caret package. Grid search is a technique where the user searches through possible combinations of hyperparameter settings to optimize performance. We will run a grid search for each hyperparameter combination and record the best-performing configuration. Afterward, we will use these best configurations to train our final model. Once the model is trained, we will evaluate its accuracy by testing it on the holdout dataset. By evaluating the model’s accuracy on the test data, we can determine if the model produced good predictions. If not, we will adjust the hyperparameters and retrain the model until we achieve a reasonable level of accuracy.

5. Model Evaluation;

Once the model reaches acceptable levels of accuracy on the test data set, we will compare it to the baseline model to determine how much better the new model performs. We will also compare the performance of each model to the baseline model to understand the relative contributions of each variable.

How to Train a Machine Learning Model in the USA

1. Data Set;

2. Models;

3. Hyperparameters;

4. Model Training;

5. Model Evaluation;

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