When is classification accuracy is not enough information?

When is classification accuracy is not enough information?

Classification accuracy alone is typically not enough information to make this decision. In this post, we will look at Precision and Recall performance measures you can use to evaluate your model for a binary classification problem. The breast cancer dataset is a standard machine learning dataset.

When do you build a model for a classification problem?

When you build a model for a classification problem you almost always want to look at the accuracy of that model as the number of correct predictions from all predictions made. This is the classification accuracy.

How is a confusion matrix used in classification?

Confusion Matrix. A clean and unambiguous way to present the prediction results of a classifier is to use a confusion matrix (also called a contingency table). For a binary classification problem the table has 2 rows and 2 columns.

How are non convex loss functions used in classification?

Such non-convex loss functions have been shown to be useful in dealing with outliers in classification. For all loss functions generated from (2), the posterior probability . Such loss functions where the posterior probability can be recovered using the invertible link are called proper loss functions.

How to test a model for classification error?

Test Error: We get this by using two completely disjoint datasets: one to train the model and the other to calculate the classification error. Both datasets need to have values for y.

How are training error and test error used in machine learning?

Training error vs test error There are two important concepts used in machine learning: the training error and the test error. Training Error: We get the by calculating the classification error of a model on the same data the model was trained on (just like the example above).

How to reshape data for a classification model?

Reshape your data either using X.reshape (-1, 1) if your data has a single feature or X.reshape (1, -1) if it contains a single sample. DeprecationWarning)