For this, you would use logistic regression. To provide estimates for classification, you need some guidance on what would be the most probable class for that data point. While linear regression is well suited for estimating continuous values (for example, estimating house prices or product sales), it is not the best tool for predicting the class in which an observed data point belongs. The difference between linear and logistic regression You can choose to create assets in Python, Scala, and R, and use open source frameworks (such as TensorFlow) that are already installed on the IBM Cloud Pak for Data as a Service platform. It also offers scalability by distributing processes across multiple computing resources. The IBM Cloud Pak for Data platform provides additional support, such as integration with multiple data sources, built-in analytics, Jupyter Notebooks, and machine learning. The Notebook runs on IBM Cloud Pak® for Data as a Service on IBM Cloud®. In this tutorial, learn how to create a Jupyter Notebook that contains Python code for defining logistic regression, then use TensorFlow (tf.keras) to implement it.
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