In this, we’ll try to cover all the aspects of logistic regression algorithm
Logistic Regression Machine Learning Regression
January 7, 2021 by Monis Khan | Leave a Comment
For selecting the right model for a given business problem, we take the following steps: Plot ROC for a given algorithm for different threshold Do this for all the other algorithms Select the algorithm which has the largest area under Receiver Operator Curve. This is the general approach but you need not always consider the […]
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Classification Logistic Regression Machine Learning
January 7, 2021 / January 7, 2021 by Monis Khan | Leave a Comment
Receiver Operator Curve (ROC) is used for finding the optimum threshold for Sensitivity. Sensitivity or Recall is a measure of rate of ‘True Positives’. It plots Sensitivity against rate of ‘False Positives’ i.e. against (1-Specificity). Research has shown that up till a certain threshold both ‘True Positive’ rate and ‘False Positive’ rate increases but beyond […]
.... Classification Logistic Regression Machine Learning
Honorable Madras High Court said that-“1000 Culprits Can Escape but, One Innocent Should Not be Punished “. More or less legal systems around the world follow this principle. The above motto shows that accuracy is not always the metric that is been sought out for. If you were to design a model that could assist […]
We know that for unbalanced datasets we can’t rely on accuracy. Here we have to measure the performance of the model using Precision & Recall. The ideal scenario would be where both Precision & Recall are high. But research has shown that the two metrics are somewhat inversely proportional to each other. I said somewhat […]
We know that for unbalanced datasets we can’t rely on accuracy. Here we have to measure the performance of the model using Precision & Recall. But the question arises, how much Precision and how much Recall. Since Precision and Recall share an inverse relationship, we can’t have the ideal scenario where both Precision & Recall […]
December 31, 2020 / December 31, 2020 by Monis Khan | Leave a Comment
Recall measures the suitability of a model for a give business statement. Precision measures the reliability of the aforementioned model. The ideal case would be when recall and precision both are high i.e. the model is suited for the business scenario and delivers high quality results. But research has shown that precision and recall share […]
December 31, 2020 / January 7, 2021 by Monis Khan | Leave a Comment
Precision measures the number of True Positives out of the Total number of positive cases predicted by the model. Precision is used to check the reliability of the model. If the precision of our model were low, we wouldn’t rely too much on the results on the model and would: Build a new model with […]
Accuracy of a machine learning model is the ratio of correct predictions to all the predictions made. The formula for accuracy using the metrics of Confusion Matrix would be.