# Analysis

**Overview**

Analysis allows you see all the important parameters governing the classifier. You can Root or individual categories to know the health of the classifier on various parameters.

**Category Tree**

This tree allows you to select Root node or individual category under the classifier. The Statistics and Confusion Matrix provides you critical information on training of your classifier.

**Statistics**

**Accuracy**

**Accuracy** is the percentage of correct predictions from total predictions made by the classifier. Note: Accuracy is only shown for non-leaf categories of the category tree.

**Precision**

**Precision** is the percentage of correct predictions out of the total predictions which fall into this category. Note: Precision and Recall are not shown for Root category.

**Recall**

**Recall** is the percentage of correct predictions out of the total test samples of the category.

**True Positive**

Number of correct predictions for the category.

**True Negative**

Number of correct negative predictions for the category.

**False Positive**

Number of incorrect predictions for the category.

**False Negative**

Number of incorrect negative predictions for the category.

**Confusion Matrix**

**Confusion matrix**, also called as Error matrix, allows easy visualization of the performance of your classifier. It’s a two dimensional matrix, where rows represent the actual class, while columns represent the predicted class or vice versa. As diagonal elements represent correct predictions, confusion matrix of an ideal classifier with 100% accuracy will be a diagonal matrix.