Jul 18, 2022 · This guide will teach you some key machine learning best practices for solving text classification problems. Here’s what you’ll learn: The high-level, end-to-end workflow for solving text classification problems using machine learning; How to choose the right model for your text classification problem

Jul 18, 2022 · For text classification datasets, we experimented with one, two, and three-layer MLPs. Models with two layers performed well, and in some cases better than three-layer models. Similarly, we tried sepCNN s with four and six layers, and the four-layer models performed well.

Apr 17, 2025 · Learn how a classification threshold can be set to convert a logistic regression model into a binary classification model, and how to use a confusion matrix to assess the four types of predictions: true positive (TP), true negative (TN), false positive (FP), and …

Jun 12, 2024 · We saw how to convert sample text data into numerical vectors. A similar process must be applied to the labels. We can simply convert labels into values in range [0, num_classes - 1]. For example, if there are 3 classes we can just use values 0, 1 and 2 to represent them.

Mar 3, 2025 · Learn how to calculate three key classification metrics—accuracy, precision, recall—and how to choose the appropriate metric to evaluate a given binary classification model.

Apr 16, 2025 · Machine learning (ML) powers some of the most important technologies we use, from translation apps to autonomous vehicles. This course explains the core concepts behind ML. ML offers a new way to solve problems, answer complex questions, and create new content.

Jun 12, 2024 · In this guide, we attempt to significantly simplify the process of selecting a text classification model. For a given dataset, our goal is to find the algorithm that achieves close to maximum accuracy while minimizing computation time required for training.

Jun 12, 2024 · Now, it’s time to write our classification algorithm and train it. We will use TensorFlow with the tf.keras API for this. Building machine learning models with Keras is all about assembling together layers, data-processing building blocks, much like …

Feb 26, 2025 · Imbalanced datasets sometimes don't contain enough minority class examples to train a model properly. That is, with so few positive labels, the model trains almost exclusively on negative labels and can't learn enough about positive labels. For example, if the batch size is 50, many batches would contain no positive labels.

Apr 2, 2025 · For example, the following two machine learning models each perform binary classification: A model that determines whether email messages are spam (the positive class) or not spam (the negative class).