Demystifying ML Modeling: A Simple Guide

The aim of this blog is to simplify one’s understanding of Machine Learning (ML) Modeling. It is not rocket-science and for sure shouldn’t feel like it.

Use Case

It all starts with a Use Case (while keeping an end goal in mind). Suppose we want to leverage Machine Learning Models to help with some real-world problems – e.g., making predictions, classifying data, or generating forecasts. In other words, our end goal is for a machine (algorithm) to autonomously predict output (values) based on input (variables). Let’s go over the steps needed to achieve this goal. On reviewing the entire process, you will appreciate that these are the same steps that a human might take to achieve the end-result manually – just that ML based solutions are more automated and scalable.

1. Data Collection

In order for us to automate any use case and for a machine to correctly execute based on input variables or attributes, the machine must first learn the correlation between input (Features) and output (Labels).

In other words, we need a Training Set (Data) to train the machines on. So, we must determine the type of data like structured or unstructured, depending on the requirements of the project, and then identify potential sources for gathering such data.

The learning could be:

Supervised Learning (labeled data),
Unsupervised Learning (Unlabeled data): machine tries to learn based on making sense of patterns in the dataset and making predictions based on analysing any new data against the learnt patterns, or
Reinforced Learning: machine/algorithm learns by trial and error while interacting with an environment and making actions – the learning is enforced by reward-penalty strategy.

Data gathering: Once the requirements are finalized, data can be collected from a variety of sources such as databases, APIs, web scraping, and manual data entry. It is crucial to ensure that the collected data is both relevant and accurate, as the quality of the data directly impacts the generalization ability of the machine learning model. In other words, the better the quality of the data, the better the performance and reliability of the model in making predictions or decisions.

2. Data Pre-Processing and Cleaning

We are able to access the Data from various sources. However, not all data is ready for consumption. We need to transform raw data into a format that is suitable for training the models e.g., remove null values, and garbage values, and normalize the data. This helps to achieve greater accuracy and performance.

3. Choosing the Right Machine Learning Model

Next, we need to pick the right ML Model. There are numerous algorithms and techniques available to choose from, and choosing the most suitable model for a given problem significantly impacts the accuracy and performance of the model.

Nature of the problem plays a critical role in selecting the right ML Model type e.g., classification, regression, clustering, etc.
Also, depending upon your use case, you may want to balance the complexity and interpretability of a model with respect to its accuracy and performance. A more complex model like Deep Learning may help in increasing the performance but is more complex to interpret.

4. Training your Machine Learning Model

At this stage, we have all the necessary ingredients to train our ML Model. Now, we feed the pre-processed data (Training Set) into the selected ML Model algorithm. The algorithm compares its predicted value with the actual target value in the training data and iteratively adjusts its internal parameters to minimize the difference (error). This process of optimization employs techniques like gradient descent.

Over time as it learns from training data, the model gradually improves it ability to react to new or unseen data and make accurate predictions across a wide range of scenarios.

5. Evaluating Model Performance

Once the model has been trained, its performance needs to be assessed. There are various metrics to do so, depending on the type of model: regression/numerical or classification. Mentioned below are some common evaluation metrics:

For regression tasks:

Mean Absolute Error (MAE): MAE is the average of the absolute differences between predicted and actual values.
Mean Squared Error (MSE): MSE is the average of the squared differences between predicted and actual values.

Root Mean Squared Error (RMSE): It is a square root of the MSE, providing a measure of the average magnitude of error.

R-squared (R2): It is the proportion of the variance in the dependent variable that is predictable from the independent variables.

For classification tasks:

Accuracy: Proportion of correctly classified instances out of the total instances.

Precision: Proportion of true positive predictions among all positive predictions.

Recall: Proportion of true positive predictions among all actual positive instances.

F1-score: Harmonic mean of precision and recall, providing a balanced measure of model performance.

Area Under the Receiver Operating Characteristic curve (AUC-ROC): Measure of the model’s ability to distinguish between classes.

Confusion Metrics: It is a matrix that summarizes the performance of a classification model, showing counts of true positives, true negatives, false positives, and false negatives instances.

6. Tuning and Optimizing

The ML model is trained but we can further optimize it and improve its performance. This involves:

fine-tuning hyper-parameters,
selecting the best algorithm, and
improving features through feature engineering techniques.

Unlike model parameters, which are learned directly from the data during training, hyperparameters are configuration variables typically set before the actual training process begins and control aspects of the learning process itself. Hyperparameters influence the model’s performance, its complexity and how fast it learns.

Some of the hyperparameters optimization techniques are GridSearchCV, RandomizedSearchCV, and Cross-Validation.

7. Deploying the Model and Making Predictions

This is the final stage in the ML Model development. We have trained and optimized the model for performance, and now we need to deploy it into production so that it can provide real-time predictions on new and unseen data.

One should follow the MLOps best practices and proven frameworks during model development to ensure that the overall solution is scalable; could handle high user loads, operate smoothly without crashes, and be easily updated.

Some of the techniques one may adopt to get the most out of their ML Models in production:

Containerization (to overcome environment and packaging related errors)
CI/CD (for automated deployment and updates)
Infrastructure as Code (IaC) for ML workflows
Serverless ML and API-based model serving

Conclusion

To summarize, ML Modeling is a simple, straight-forward process once you understand the core steps – Data Collection and Pre-Processing, Selecting the right ML Model, Training it, Evaluating its performance, Tuning/Optimizing it, and finally Deploying it to Production, so that the model can be leveraged to make predictions and solve for real-world use cases.

At NeuralChainAI, we have the expertise to help you with any of your ML Modeling challenges.

Explore our ML Modeling case studies here: