What are the steps taken in training a machine learning model?

Today, as we evolve with the latest technology and go deeper into the digital era, the potential of machine learning is more evident than ever. These machine learning models have become an integral part of our day-to-day lives with applications in the form of virtual assistants like Siri and Alexa and social media recommendations on almost every application on our smartphones. These applications extend to the field of medicine in diagnostics and in industries for product maintenance and quality control.

Before discussing the steps taken to train a machine learning model, let’s first discuss what a machine learning model does.

A machine learning model is a mathematical representation or algorithm that learns patterns from the given training data and generalizes these predictions accurately on new, unseen data.

Now, that we know what an ML model is, let’s explore the steps taken in training one.

Data collection

The initial step in training a machine learning model is to identify the problem statement and gather the necessary requirements. Then, according to these requirements, relevant data is collected. ­­

It is necessary that the data matches the requirements as accurately as possible as the ML model's only input is the data we feed into it, and on whose basis, predictions and classifications are conducted.

Let’s take a look at the figure below that outlines some key considerations to keep in mind when collecting data for model training.

Data preprocessing

The second stage in training an ML model is the data processing steps. This is a crucial stage that involves transforming raw data into a format that is suitable for model enhancing the performance of the ML algorithms.

Let's discuss the steps involved in data preprocessing in detail:

• Data completion: It refers to the process of handling missing values in a dataset which can occur due to malfunction of sensors, or incomplete data collection. Techniques such as mean imputation, and regression imputation are used to complete the missing data

• Data transformation: It involves changing the scale, distribution, or format of the data to meet specific requirements of the model. Data transformation can include scaling and normalization of the data.  

• Data noise reduction: It is the process of removing unwanted random variations or errors, known as noise, from a dataset that may arise from measurement errors or data collection inconsistencies. Some common techniques used for data noise reduction are smoothing and filtering of the raw data.

Feature extraction

After completing the data processing steps, we conduct feature extraction, which is a technique used to reduce a large input data set into relevant features. This is done with dimensionality reduction to transform large input data into smaller, meaningful groups for processing. Some common techniques include the principle components analysis (PCA) and the independent component analysis (ICA).

Note: Preprocessing and feature extraction are important phases in the training of a machine learning model as poor data input leads to inaccuracy in the model.

Training the model

Training a machine learning model includes choosing the right model suited to your requirements according to the tasks that you want to perform, such as regression, classification or clustering, etc.

Before preceding further, let's go through some key terms for model training:

• Batch: A subset of training data used to update the model's parameters during one iteration.

• Epoch: A complete pass through the entire training dataset during training.

• Iteration: The number of batches needed to complete one epoch.

Once the model is selected, it is initialized with weights and bias i.e. parameters. Then, during the training process of a machine learning model, the data is typically split into training and testing sets. The training set is used to train the model, while the testing set is used to evaluate its performance.

The training data is further divided into batches, and the model undergoes multiple iterations within each epoch. With each iteration, the model makes predictions on a batch of training data, calculates the associated loss, computes gradients, and updates its parameters accordingly. These iterations continue until all batches within an epoch have been processed.

By repeating the batch-epoch-iteration cycle for multiple epochs, the model gains a comprehensive understanding of the training data and optimizes its parameters to minimize loss function to make accurate predictions on new, unseen data.

Evaluating the model

After training the model, the next step is to evaluate the trained model on new unseen data to determine its ability to make accurate predictions. The test data is usually used to predict the accuracy of the model. The model's predicted value and the actual value of the test set are used in the loss function to measure the accuracy of the model. Common cost functions include mean squared error and binary cross entropy.

This information helps in making informed decisions about model deployment, fine-tuning hyperparameters, and identifying any issues like overfitting or underfitting for which the model is optimized.

Hyperparameter tuning

Once the model is evaluated, we see if its accuracy can be improved in any way. This is done by tuning the parameters present in your model. Parameters are the internal variables or weights that a machine learning model learns during the training process. Hyperparameter tuning refers to finding the particular value at which the accuracy of our model will be the maximum.

Making predictions

In the end, our model is ready to make predictions on new, unseen data, accurately.

Conclusion

In conclusion, training a machine learning model involves a series of important steps. It begins with collecting and preprocessing the data, which includes data cleaning, feature engineering, and handling missing values. The next step is to select an appropriate model architecture that suits the problem at hand. This is followed by splitting the data into training and testing sets to evaluate the model's performance. Lastly, hyperparameter tuning is essential to optimize the model's parameters and achieve the best possible results.