There are many ways to address missing data, each with pros and cons. Let’s take a look at the less complex options: Option 1: Delete data with missing rows. When we have a model that cannot handle missing data, the most prudent thing to do is to remove rows that have information missing. Let’s remove…
Do we have a complete dataset in a real-world scenario? No. We know from history that there is missing information in our data! How can we tell if the data we have available is complete? We could print the entire dataset, but this could involve human error, and it would become impractical with this many…
Simple models with small datasets can often be fit in a single step, while larger datasets and more complex models must be fit by repeatedly using the model with training data and comparing the output with the expected label. If the prediction is accurate enough, we consider the model trained. If not, we adjust the…
It’s worth keeping in mind that train-and-test is common, but not the only widely used approach in machine learning. Two of the more coming alternatives are the hold-out approach and statistical approach methods. hese statistical methods are powerful, well established, and form the foundation of modern science. The advantage is that the training set doesn’t…
A model is overfit if it works better on training data than it does on other data. verfitting can be avoided in several ways. The simplest way is to have a dataset that’s a better representation of what is seen in the real world. A complimentary way we can avoid overfitting is to stop training…
The workflow for implementing Artificial Intelligence and Machine Learning solutions typically involves several stages. Collaborative efforts among data scientists, domain experts, and stakeholders are crucial throughout the process. The specific details of the workflow can vary based on the complexity of the problem, the type of algorithm used, and the specific requirements of the project.…
