Binary classification dataset matlab, It includes MATLAB implementations, 10-fold cross-validation, and visualizations of decision boundaries and MSE histories. To read data via MATLAB, you can use "libsvmread" in LIBSVM package. For greater flexibility, you can pass predictor or feature data with corresponding responses or labels to an algorithm-fitting function in the command-line This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary classification tasks using linearly, non-linearly, and marginally separable datasets from the Iris dataset. Feb 16, 2024 · About Dataset Description: The "Campus Placement Prediction" dataset encapsulates a comprehensive array of attributes aimed at predicting the outcome of candidate selection during campus placement processes. 🛡️ Threat Categories (type) The dataset is well-balanced across various attack vectors, offering a diverse set of network anomalies: normal: 50,000 records backdoor: 20,000 records ddos Feb 14, 2026 · About Dataset 📖 Description The Fake vs Real News (4K Articles) dataset is a structured and fully cleaned collection of 4000 news articles labeled for binary classification tasks. Datasets New Dataset filter_list Filters Binary Classification close table_chart Hotness view_list calendar_view_month Classification is a type of supervised machine learning in which an algorithm “learns” to classify new observations from examples of labeled data. ClassificationLinear is a trained linear model object for binary classification; the linear model is a support vector machine (SVM) or logistic regression model. - iqiukp/BinaryDataset Support Vector Machines for Binary Classification Understanding Support Vector Machines Separable Data Nonseparable Data Nonlinear Transformation with Kernels Separable Data You can use a support vector machine (SVM) when your data has exactly two classes. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. MATLAB code for 2D or 3D binary dataset for classification. CIFAR-10 and CIFAR-100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Some training data are further separated to "training" (tr) and "validation" (val) sets. The best . The testing data (if provided) is adjusted accordingly. The CIFAR-10 and CIFAR-100 datasets are labeled subsets of the 80 million tiny images dataset. Developed at AT&T Bell Laboratories, [1][2] SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and 3 days ago · Target Variables: * label: Binary classification (0 = Normal, 1 = Malicious) type: Multi-class classification defining the specific attack category. An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of the other class. Details can be found in the description of each data set. Each article is categorized as either Fake (0) or Real (1), making this dataset ideal for developing and evaluating fake news detection models. train a Generalized Additive Model (GAM) for Binary Classification with optimal parameters and how to assess the predictive performance of the trained model. In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. To explore classification models interactively, use the Classification Learner app. Jul 11, 2025 · This project contains two comprehensive examples of binary classification using feedforward neural networks in MATLAB. The implementations demonstrate fundamental machine learning concepts including synthetic data generation, neural network training, prediction, and decision boundary visualization.


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