Archives for classification problem

05 Sep

Understanding the AUC-ROC Curve in Machine Learning Classification

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image-25883

AUC-ROC is the valued metric used for evaluating the performance in classification models. The AUC-ROC metric clearly helps determine and tell us about the capability of a model in distinguishing the classes. The judging criteria being - Higher the AUC, better the model. AUC-ROC curves are frequently used to depict in a graphical way the connection and trade-off between sensitivity and specificity for every possible cut-off for a test being performed or a combination of tests being performed. The area under the ROC curve gives an idea about the benefit of using the test for the underlying question. AUC - ROC curves are also a performance measurement for the classification problems at various threshold settings. 

The post Understanding the AUC-ROC Curve in Machine Learning Classification appeared first on Analytics India Magazine.

16 Jul

Guide To VOLO: Vision Outlooker For Visual Recognition

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image-24294

A neural network is a system of software patterned similar to the operation of neurons in a human brain. However, traditional and simple neural networks are not ideal for image processing and need images to be fed in reduced resolution divided into pieces. Convolutional Neural Networks or CNN are popularly known as deep learning architecture…

The post Guide To VOLO: Vision Outlooker For Visual Recognition appeared first on Analytics India Magazine.

10 Jun

Python Guide to Precision-Recall Tradeoff

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image-23438

What do you think should we consider only the accuracy score as a benchmark for our classification task? Many beginners in this field have misunderstood; getting good accuracy for classification models means they have built a perfect model which classifies every instance. Well, you can consider only accuracy as a benchmark for regression problems. For…

The post Python Guide to Precision-Recall Tradeoff appeared first on Analytics India Magazine.

04 Nov

Hands-On Implementation Of Perceptron Algorithm in Python

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image-17322

Perceptron is the first neural network to be created. It was designed by Frank Rosenblatt in 1957. Perceptron is a single layer neural network. This is the only neural network without any hidden layer. Perceptron is used in supervised learning generally for binary classification.

The post Hands-On Implementation Of Perceptron Algorithm in Python appeared first on Analytics India Magazine.