Five Major Steps in the Machine Learning Process
In this article, I will walk you through the five significant steps of building a Machine Learning model.
An application of AI called machine learning allows systems to learn from the past performance without explicitly being programmed. Machine learning aims to create computer programs that can access data and use it to acquire knowledge on their own. Machine learning relies on input, such as training data or knowledge graphs, to comprehend things, domains, and the connections between them, much to how the human brain acquires information and understanding.
Machine learning professionals follow a standard methodology to complete tasks, regardless of the model or training technique used. These actions involve iteration. This means that you evaluate how the process progresses at each stage. Are things going as you anticipated? If not, go back and review your previous steps or current step to try to figure out where the breakdown occurred.
The task of imparting intelligence to machines seems daunting and impossible. But it is easy. It can be broken down into five significant steps :
- Define the problem
- Build the dataset
- Train the model
- Evaluate the model
- Inference(Implementing the model)
1. Define the problem
The first step in the machine learning process is defining the problem. When approaching any problem through machine learning, it is always necessary to be specific about the area you will focus on.
For example, if you want to analyze the process to increase sales. You cant take the entire thing as your problem. You are specific such as "Does adding a $1.00 charge for a special add-on increase the sales of that product?". When you define the problem in such a way, it will be easy for you to choose the machine learning task and the nature of the data needed to build the model.
What is a Machine Learning Task?
A machine learning task is a prediction or inference produced in response to a problem or issue and the currently available data. For instance, the clustering task groups data based on similarity, whereas the classification task allocates data to categories.
In common, there are three major machine learning tasks:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised tasks
If you are using labeled data, the task is being supervised. The data that already has the answers, or labels, is referred to as being labeled. Data in this situation can be categorized or continuous. Regression, categorical, or other supervised learning methods can solve these problems.
Unsupervised tasks
If you use unlabeled data for a task, it is deemed unsupervised. This means that while the model is being trained, you do not need to give it any labels or solutions. We can use clustering algorithms to classify the data for these issues by the hidden patterns deduced from the data.
Reinforcement tasks
An agent is trained to accomplish a goal via reinforcement learning (RL) based on the feedback it receives from its interactions with the environment. For each activity it performs, it gains a specific number as compensation. Higher numbers are rewarded for actions that aid the agent in achieving its objective. Unhelpful behavior yields a meager or no reward.
2. Build a Dataset
Building a dataset that can be utilized to address your machine learning-based challenge is the following step in the machine learning process. Understanding the necessary data enables you to choose superior models and algorithms, resulting in the development of more efficient solutions. Working with information is perhaps the most overlooked — yet most important — step of the machine learning process.
The Four Aspects of Working with Data
Data collection
Gathering data for your project can be as simple as running the appropriate SQL queries or as complex as developing custom web scraper applications. You may even need to run a model on your data to obtain the required labels.
Data inspection
The most crucial component that will eventually influence how well you anticipate your model to perform is the quality of your data. As you inspect your data, look for:
- Outliers
- Missing or incomplete values
- Data that needs to be transformed or preprocessed
So it's in the correct format to be used by your model
Summary statistics
A subset of descriptive statistics called summary statistics gives an overview of the information about the sample data. It is the goal of summary statistics to summarize statistical data. This shows that summary statistics can be effectively used to quickly grasp the essence of the data. Statistics typically deals with the quantitative or visual display of information.
Data visualization
To help people comprehend and make sense of massive volumes of data, data visualization is a technique that uses a variety of static and dynamic visualizations within a given context. The data is sometimes presented in a story format to visualize patterns, trends, and connections that could otherwise go missing.
3. Model Training
After we prepare the data, the next step of our process is to do the model training using the data we have prepared. We will split the data into two major categories as the initial step.
Splitting your dataset gives you two sets of data:
- Training dataset: The data on which the model will be trained. Most of your data will be here. Many developers estimate about 80%.
- Test dataset: The data withheld from the model during training is used to test how well your model will generalize to new data.
After splitting, we can use the dataset to train the model we prefer. Use machine learning frameworks that provide model implementations and model training algorithms that are currently operational. Unless you're creating new models or algorithms, you usually won't need to implement these from scratch.
Pick a model or some models using a method known as model selection. Even seasoned machine learning practitioners may experiment with many different models while using machine learning to solve problems because the number of recognized models continually expands. Hyperparameters are model settings that are left unchanged throughout training but may impact how quickly or accurately the model learns, such as the number of clusters it should be able to recognize.
The end-to-end training process is
- Feed the training data into the model.
- Compute the loss function on the results.
- Update the model parameters in a direction that reduces loss.
You continue to cycle through these steps until you reach a predefined stop condition. This might be based on a training time, the number of training cycles, or an even more intelligent or application-aware mechanism.
4. Model Evaluation
You can assess how well your model functions once you have gathered your data, trained a model, and then used it. The parameters employed for review are probably quite particular to your identified issue. You will be able to investigate a wide range of indicators that can help you evaluate effectively as your knowledge of machine learning increases. There are many evaluation matrices on which we can decide the model's performance. Those are such as
- Accuracy
- Specificity
- Recall or Sensitivity
- F1 Score
- Precision
5. Model Inference
You are prepared to make predictions about problems in the real world using data that has not yet been observed in the field once you have trained your model, assessed its efficacy, and are satisfied with the results. This procedure is frequently referred to as inference in machine learning. Using a trained model to infer conclusions from current data is known as model inference. The model inference is merely the processing of omitted data using the trained model to create a result, albeit the results may be monitored for future optimization. Even after your model has been deployed, you keep an eye on it to ensure it is generating the outcomes you are looking for. You might need to reexamine the data, adjust a few settings in your model training procedure, or switch the trained model type.
Remember that this process is Iterative.
Each step has been highly iterative and is subject to alteration or re-scoping as a project progresses. You could discover that you need to go back and review some assumptions you made in earlier steps at each level. This uncertainty is expected. When the evaluation is not as expected, it's okay, and we can go back to do the alterations and re-train. Then we iterate the process repeatedly to achieve the required precision and accuracy.
I believe that the article above will give you insight into your journey of building machine learning models. Don't stop after reading. Just try to implement the steps in your next project and share your valuable suggestions.
