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Asked on October 10, 2020 in Data Science.
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.
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Asked on October 10, 2020 in Data Science.
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Asked on October 10, 2020 in Data Science.
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 ndimentional 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.
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Asked on October 10, 2020 in Data Science.
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Asked on October 10, 2020 in Data Science.
The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier. Various measures, such as errorrate, 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)
FScore(Harmonic mean of precision and recall) = (1+b)(PREC.REC)/(b^2PREC+REC) where b is commonly 0.5, 1, 2.
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