# Binary classification dataset examples

**classification**machine learning algorithm. It works by calculating summary statistics for the input features by class label, such as the mean and standard deviation. These statistics represent the model learned from the training data. In practice, linear algebra operations are used to.

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**example**notebook, we prepared the SST2

**dataset**in the public SageMaker sample file S3 bucket. The following code cells show how you can directly load the

**dataset**and convert to a HuggingFace DatasetDict. Preprocessing We download and preprocess the SST2

**dataset**from the s3://sagemaker-sample-files/

**datasets**bucket. Aug 27, 2021 · In this blog, I would like to.

**Binary classification**refers to a subset of these problems in which there are two possible outcomes. Given some variables \ (X_1, ..., X_n\), we want to predict the probability.

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**binary**label. In

**classification**problems, the label for every

**example**must be either 0 or 1. Unfortunately, the natural label in the California Housing

**Dataset**, median_house_value, contains floating-point values like 80,100 or 85,700 rather than 0s and 1s, while the normalized version of median_house_values contains floating-point values primarily between -3 and +3. This tutorial demonstrates how to train a text classifier on SST-2

**binary**

**dataset**using a pre-trained XLM-RoBERTa (XLM-R) model. We will show how to use torchtext library to: read SST-2

**dataset**and transform it using text and label transformation. instantiate

**classification**model using pre-trained XLM-R encoder.

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**Binary Classification**. Here we describe a very simple TransmogrifAI workflow for predicting survivors in the often-cited Titanic

**dataset**. The code for building and applying the Titanic model can be found here, and the data can be found here. You can run this code as follows:. Here we need to remember some basic aspects of the possible machine.