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• Asked on October 10, 2020 in

Both Regression and classification machine learning techniques come under Supervised machine learning algorithms. In Supervised machine learning algorithm, we have to train the model using labeled dataset, While training we have to explicitly provide the correct labels and algorithm tries to learn the pattern from input to output. If our labels are discreate values then it will a classification problem, e.g A,B etc. but if our labels are continuous values then it will be a regression problem, e.g 1.23, 1.333 etc.

• 7 views
• Asked on October 10, 2020 in In the above diagram we see that the thinner lines mark the distance from the classifier to the closest data points called the support vectors (darkened data points). The distance between the two thin lines is called the margin.

• 4 views
• Asked on October 10, 2020 in

SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training dataset, SVM tries to plot it in n-dimentional space with the value of each feature being the value of a particular coordinate. SVM uses hyper planes to seperate out different classes based on the provided kernel function. • 5 views
• Asked on October 10, 2020 in

The ROC curve is a graphical representation of the contrast between true positive rates and false positive rates at various thresholds. It is often used as a proxy for the trade-off between the sensitivity(true positive rate) and false positive rate. • 5 views
• Asked on October 10, 2020 in

The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion Matrix A dataset used for performance evaluation is called test dataset. It should contain the correct labels and predicted labels. The predicted labels will exactly the same if the performance of a binary classfier is perfect. The predicted labels usually match with part of the observed labels in real world scenarios. A binary classifier predicts all data instances of a test dataset as either positive or negative. This produces four outcomes-

True positive(TP) – Correct positive prediction

False positive(FP) – Incorrect positive prediction

True negative(TN) – Correct negative prediction

False negative(FN) – Incorrect negative prediction Basic measures derived from the confusion matrix

Error Rate = (FP+FN)/(P+N)

Accuracy = (TP+TN)/(P+N)

Sensitivity(Recall or True positive rate) = TP/P

Specificity(True negative rate) = TN/N

Precision(Positive predicted value) = TP/(TP+FP)

F-Score(Harmonic mean of precision and recall) = (1+b)(PREC.REC)/(b^2PREC+REC) where b is commonly 0.5, 1, 2.

• 4 views