Category: Machine Learning

Akmal Chaudhri tries a bunch of things:

In this short article, we’ll explore loan approvals using a variety of tools and techniques. We’ll begin by analyzing loan data and applying Logistic Regression to predict loan outcomes. Building on this, we’ll integrate BERT for Natural Language Processing to enhance prediction accuracy. To interpret the predictions, we’ll use SHAP and LIME explanation frameworks, providing insights into feature importance and model behavior. Finally, we’ll explore the potential of Natural Language Processing through LangChain to automate loan predictions, using the power of conversational AI.

Click through for the notebook, as well as an overview of what the notebook includes. I don’t particularly like word clouds as the “solution” in the BERT example, though without real data to perform any sort of NLP, there’s not much you can meaningfully do.

Bala Priya C shares some tips and techniques:

If you’re familiar with machine learning, you know that the training process allows the model to learn the optimal values for the parameters—or model coefficients—that characterize it. But machine learning models also have a set of hyperparameters whose values you should specify when training the model. So how do you find the optimal values for these hyperparameters?

You can use hyperparameter tuning to find the best values for the hyperparameters. By systematically adjusting hyperparameters, you can optimize your models to achieve the best possible results.

This tutorial provides practical tips for effective hyperparameter tuning—starting from building a baseline model to using advanced techniques like Bayesian optimization. Whether you’re new to hyperparameter tuning or looking to refine your approach, these tips will help you build better machine learning models. Let’s get started.

Read on for those techniques. Incidentally, one of my “Old man yells at clouds” takes is that I dislike the existence of hyperparameters and consider them a modeling failure, essentially telling the implementer to do part of the researcher’s work. Knowing that they are necessary to work with for so many algorithms, there’s nothing to do but learn how to work with them effectively, but there’s a feel of outsourcing the hard work to users that I don’t like about the process. For that reason, I have extra respect for algorithms that neither need nor offer hyperparameters.

Abid Ali Awan gives us a primer on TPOT:

AutoML is a tool designed for both technical and non-technical experts. It simplifies the process of training machine learning models. All you have to do is provide it with the dataset, and in return, it will provide you with the best-performing model for your use case. You don’t have to code for long hours or experiment with various techniques; it will do everything on its own for you.

In this tutorial, we will learn about AutoML and TPOT, a Python AutoML tool for building machine learning pipelines. We will also learn to build a machine learning classifier, save the model, and use it for model inference.

Click through to see an example of how to use the library.