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4 days ago · Classification is a type of problem in machine learning where we want to “classify” data into categories. For example, given an email, we can classify it as “spam” or “not spam.” It’s about predicting the correct category or label based on the input data.
2 days ago · Supervised learning is a category within the machine learning realm defined by its use of models that train with labeled data to make predictions or classify new data. Within the labeled data, features exist as the input, and targets exist as the output. With these inputs and outputs, the model trains to discover the mapping between them to ...
1 day ago · Zero-shot classification represents a significant advancement in machine learning, enabling models to adapt to new categories without retraining. By addressing the challenges associated with attribute collection and semantic gaps, the integration of SSL and zero-shot learning can lead to more efficient and generalized vision models.
2 days ago · The confusion matrix is a powerful tool for assessing the performance of classification algorithms in machine learning. Providing a comprehensive comparison between actual and predicted values enables us to evaluate our models’ accuracy, precision, recall, and other performance metrics.
4 days ago · In this article, we will be creating an artificial neural network from scratch in python. The Artificial Neural Network that we are going to develop here is the one that will solve a classification problem. So stretch your fingers, and let’s get started.
5 days ago · Classification is a type of supervised learning algorithm in which incoming data is classified or labeled based on past data. The algorithm is trained by feeding it with different types of labeled data that can be categorized into different outcomes.
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3 days ago · From a taxonomic point of view, these techniques are classified into filter, wrapper, embedded, and hybrid methods. Now, let’s discuss some of these popular machine learning feature selection methods in detail.
