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Confusion Matrix for Classification Model Evaluation A confusion matrix is a n x n matrix used to describe the performance of a classification model (where n is the number of labels). In the confusion matrix, each row represents an actual class, while each column represents a predicted class.

Top Logistic Regression Classification Algorithms in Machine Learning:

  • Bayesian naiveté.
  • K-Kindest Neighbors
  • The Decision Tree
  • Vector Machines should be used.

The quality of a statistical or machine learning model is measured using evaluation metrics. Any project requires the evaluation of machine learning models or algorithms. There are numerous evaluation measures available for testing a model.

The Classification method is a Supervised Learning technique that uses training data to identify the category of new observations. A software in Classification learns from a given dataset or observations and then classifies additional observations into one of several classes or groupings.

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