Six steps to a successful machine learning model

Last updated on August 26th, 2022

A machine learning (ML) model is created by running training data through a number of different methods or algorithms so they can learn to handle new, unseen data. After being trained and tuned, models are tested and the ones that perform the best can be used in your application. We can break down how to develop ML solutions into six steps.

Step 1: Identify the problem you want to solve

Before you can start to build a machine learning model, you first need to determine what problem or use case you’re trying to solve and what data you have available for it. Are you trying to determine if newly manufactured motors have defects? Do you want to understand the cause of your defects? The problem you are trying to solve will impact how you treat your data and which ML models you can use.

Step 2: Identify your data inputs and expected outputs

Once you’ve clearly defined the problem, you need to assess the data you have available. Does your data contain the outputs you are looking to detect or predict? If you’re looking for defective parts, does the data contain labels for defects? Also consider the relationships between inputs and outputs. We have to assume that there is a relationship between the two, and that we have sufficient data to learn these relationships. Machine learning algorithms rely on patterns being present in the data. If relationships are not present, the models won’t be able to determine the outputs themselves. Finally, define what success looks like.

Step 3: Transform your data so it can be used by a machine learning model

Before we can apply a machine learning model to a dataset, we have to clean the data. We remove all duplicate, null, or missing values, map continuous and discrete features, standardize, normalize, and identify redundancies in the dataset.

Step 4: Divide your data into training and test sets

The denser the dataset, the better the trained models will perform. Dense datasets contain valuable information and relationships, and they’re generally larger. On the other hand, if the dataset contains limited information and no valuable relationships, then the trained models will not be effective, no matter how much data you have. Once we have a clean dataset, we randomly divide the data into two parts: a training set and a test set. We teach the model with the training set and evaluate its performance on the test set. So, make sure there is no data leakage between these sets: none of the test data should be found in your training set.

In models that are trying to classify an object, like determining whether or not a manufactured part is defective, we divide the data into training and test sets randomly but we preserve the proportion of defective and non-defective parts in each set. If we’re trying to predict the future, like when a tool is going to wear out, we have to divide the data carefully as the time sequence is crucial.

Step 5: Select a machine learning algorithm that fits your problem

Depending on the problem you’re trying to solve, a different set of machine learning algorithms can be leveraged. If we go back to our defective parts example, the problem we are trying to solve is to automatically classify parts as either defective or not. For this, we would choose a classification model, such as K-nearest neighbours, where data points are grouped by proximity. If our problem was to predict tool wear, we would need a regression model such as linear regression or a variety of neural networks. 

Step 6: Find the model with the best performance

Once you know which models you can use, you need to tune their performance to meet your desired outcomes. Tuning involves adjusting the learning parameters and the model architecture parameters to achieve a desired performance. Let’s look at our defective parts example again. For a binary classification example like this, to analyze performance and the trade-off between false positive and true negative results, we can use AUC: area under the ROC curve. A number of different models can be tuned and their performances compared using this metric before a final configured model is chosen for deployment.

Ready to get started with machine learning models for manufacturing?

This 6-step overview is just the tip of the iceberg when it comes to machine learning. Our machine learning and artificial intelligence (ML/AI) solutions help manufacturers of precision parts for automotive and off-highway vehicles make the right decisions fast, improve product quality, reduce scrap and rework, and optimize production.

Get in touch to learn how our solutions will help your manufacturing line.

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