CS4491/CS 7265 BIG DATA ANALYTICS
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1 CS4491/CS 7265 BIG DATA ANALYTICS EVALUATION * Some contents are adapted from Dr. Hung Huang and Dr. Chengkai Li at UT Arlington Dr. Mingon Kang Computer Science, Kennesaw State University
2 Evaluation for Classification
3 Evaluation Metrics Confusion Matrix: shows performance of an algorithm, especially predictive capability. rather than how fast it takes to classify, build models, or scalability. Predicted Class Actual Class Class = YES Class = No Class = Yes True Positive False Negative Class = No False Positive True Negative
4 Evaluation Metrics
5 Type I and II error
6 Evaluation Metrics Sensitivity or True Positive Rate (TPR) TP/(TP+FN) Specificity or True Negative Rate (TNR) TN/(FP+TN) Precision or Positive Predictive Value (PPV) TP/(TP+FP) Negative Predictive Value (NPV) TN/(TN+FN) Accuracy (TP+TN)/(TP+FP+TN+FN)
7 Limitation of Accuracy Consider a binary classification problem Number of Class 0 examples = 9990 Number of Class 1 examples = 10 If predict all as 0, accuracy is 9990/10000=99.9% Precision Recall Weighted Accuracy = w TP TP+w TN TN w TP TP+w FP FP+w TN TN+w FN FN
8 ROC curve Receiver Operating Characteristic Graphical approach for displaying the tradeoff between true positive rate(tpr) and false positive rate (FPR) of a classifier TPR = positives correctly classified/total positives FPR = negatives incorrectly classified/total negatives TPR on y-axis and FPR on x-axis
9 ROC curve
10 ROC curve
11 ROC curve Points of interests (TP, FP) (0, 0): everything is negative (1, 1): everything is positive (1, 0): perfect (ideal) Diagonal line Random guessing (50%) Area Under Curve (AUC) Measurement how good the model on the average Good to compare with other methods
12 Evaluation Model Selection How to evaluate the performance of a model? How to obtain reliable estimates? Performance estimation How to compare the relative performance with competing models?
13 Motivation We often have a finite set of data If using the entire training data for the best model, The model normally overfits the training data, where it often gives almost 100% correct classification results on training data Better to split the training data into disjoint subsets Note that test data is not used in any way to create the classifier Cheating!
14 Methods of Validation Holdout Use 2/3 for training and 1/3 for testing Cross-validation Random subsampling K-Fold Cross-validation Leave-one-out Stratified cross-validation Stratified 10-fold cross-validation is often the best Bootstrapping Sampling with replacement Oversampling vs undersampling
15 Holdout Split dataset into two groups for training and test Training dataset: used to train the model Test dataset: use to estimate the error rate of the model Entire dataset Split the data into two Training set Test set Drawback When unfortunate split happens, the holdout estimate of error rate will be misleading
16 Random Subsampling Split the data set into two groups Randomly selects a number of samples without replacement Usually, one third for testing, the rest for training
17 K-Fold Cross-validation K-fold Partition Partition K equal sized sub groups Use K-1 groups for training and the remaining one for testing Test set Experiment 1 Experiment 2 Experiment 3 Experiment 4 Experiment 5
18 K-fold cross-validation Suppose that E i is the performance in the i-th experiment The average error rate is k E = 1 K i=1 E i
19 Leave-one-out cross-validation Use N-1 samples for training and the remaining sample for testing (i.e., there is only one sample for testing) The average error rate is N E = 1 N i=1 E i where N is the total sample number.
20 How many folds? If a large number of folds Bias to the true estimator will be small. The estimator will be accurate Computationally expensive If a small number of folds Cheap computational time for experiments Variance of the estimator will be small Bias will be large 5 or 10-Fold CV is a common choice for K-fold CV
21 Stratified cross-validation When randomly selecting training or test sets, ensure that class proportions are maintained in each selected set. 1. Stratify instances by class 2. Randomly select instances from each class proportionally Ref:
22 Bootstrapping Oversampling Amplifying the minor class samples so that the classes are equally distributed
23 Bootstrapping Undersampling Consider less numbers of samples in the major class so that the classes are equally distributed
24 Thinking of experimental results
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