We hope all our blogs on data preparation and processing have helped you reach this next stage of your Machine Learning Pipeline, which is the actual development of the model. The development of an ML model starts with the architecture design of the model and the first-ever training of the model, which eventually leads to hyperparameter tuning. In this blog, we will discuss the absolute basic mistakes you should avoid while training your Machine Learning model.
Training a machine learning model can be a complex and time-consuming process. Despite the efforts put into training a model, it is common for machine learning practitioners to make mistakes that can hinder the model's performance. In this blog post, we will discuss five common mistakes made during the machine learning model training process and how to avoid them.
So let us dive into the five mistakes that can be avoided through some very simple steps.
One of the essential steps in machine learning is the cleaning and preprocessing the data. Raw data is often messy and may contain errors, missing values, and inconsistencies that can negatively impact the model's performance. Therefore, it is essential to spend time cleaning and preprocessing the data to ensure that it is high quality and ready for modeling. This can involve filling in missing values, removing duplicates, and scaling numerical features. To learn more about dealing with this, check out “Data Collection and Processing”.
You can start working towards better data with a thorough Data Exploration which will help you identify the best features required for your machine learning pipeline. Data exploration is the approach you’ll need when looking at your data. It enables you to gain insight into what your data means. It translates to creating a bunch of tables, visuals, and statistics that will help the team make sense of these never-ending arrays and lists of data.
Overfitting and underfitting are two common problems that occur during the training process. Overfitting occurs when the model is too complex and can fit the training data very well but cannot generalize to new data. On the other hand, underfitting occurs when the model needs to be more complex and fit the training data well.
To avoid overfitting and underfitting, selecting a simple model for the data is important. One way to do this is to use cross-validation to evaluate the model's performance on different subsets of the training data. Additionally, regularization techniques, such as adding a penalty term to the objective function, can help prevent overfitting.
Another mistake that is commonly made when training machine learning models is not using enough data. Machine learning algorithms are able to learn patterns and relationships in the data, but they need a sufficient amount of data to do so. If the training data is too small, the model may not be able to learn effectively and may have poor performance. It is important to have a large and diverse dataset to train the model on to ensure that it is able to learn effectively.
However, too much data can also lead to discrepancies in your model training. This phenomenon is called data leakage, where the model is trained on much more data and features than what will be available when the model is out in the world.
Hyperparameters are parameters that are set before the training process begins and are not learned during training. Examples of hyperparameters include the learning rate and the regularization coefficient. Not tuning hyperparameters can lead to suboptimal model performance. It is important to spend time tuning hyperparameters to find the best values for the problem. This can be done using techniques such as grid search or random search.
Hyperparameter Tuning is an essential part of taming a wild machine learning model and is necessary to ensure that the pipeline isn’t producing sub-optimal results that inhibit a model from not working at its full potential. We assume that the full potential of a model refers to its very ability to minimize the decreed loss function as low as possible without actually over-fitting the dataset.
Finally, not evaluating the model's performance is a common mistake that is made during the training process. It is important to evaluate the model's performance on a separate test dataset to see how well it generalizes to new data. This will give you a good idea of how well the model will perform in the real world. Additionally, it is important to use appropriate metrics to evaluate the model's performance. For example, if the goal is to predict a binary outcome, such as whether a customer will churn or not, then a classification metric, such as precision or recall, would be appropriate.
Most MLOps pipelines implode when the developer jumps to real-life applications. This method of not testing the accuracy and performance of a model first in a controlled environment leads to various anomalies from the desired functionality of a model. How about going through “Performance Metrics to Monitor in Machine Learning Projects” to learn more about implementing such metrics in your own ML model?
In conclusion, avoiding these common mistakes can help you to build a successful machine learning model. By cleaning and preprocessing the data, avoiding overfitting and underfitting, using enough data, tuning hyperparameters, and evaluating the model's performance, you can increase the chances of building a high-performing machine learning model.
Want to unlock $350K in cloud credits and take your ML efforts to the next level? NimbleBox.ai is here to help. We’ll help you blast through model deployment 4x faster and reduce your headache of infra management by 80%. NimbleBox makes it a breeze to develop and deploy ML models in production.
Want to learn more? Let’s discuss how NimbleBox can support your ML project.