Background:
Some background information of the electricity consumption in India. Electricity consumption (demand) in the country will grow at 7.1% (CAGR) between FY17 and FY22 and then slow to 6% in the subsequent five years, according to the Central Electricity Authority (CEA). Tamil Nadu has joined 3 other states, Maharashtra, Gujarat, and Uttar Pradesh, in India whose average power consumption per day is more than 300 million units. It's power supply has increased from 200 million units in 2011 to around 315 million units per day in 2017.
We provide real time readings for residential,commercial,industrial,agriculture to find the accuracy consumption in Tamil Nadu Around Thanajvur. This practice is dealing with accurate predictions of the electricity consumption in Tamil Nadu.
The evaluation of this dataset is done using Area Under the ROC curve (AUC).
An example of its application are ROC curves. Here, the true positive rates are plotted against false positive rates. An example is below. The closer AUC for a model comes to 1, the better it is. So models with higher AUCs are preferred over those with lower AUCs.
Please note, there are also other methods than ROC curves but they are also related to the true positive and false positive rates, e. g. precision-recall, F1-Score or Lorenz curves.
AUC is used most of the time to mean AUROC, AUC is ambiguous (could be any curve) while AUROC is not.
The AUROC has several equivalent interpretations:
Assume we have a probabilistic, binary classifier such as logistic regression.
Before presenting the ROC curve (= Receiver Operating Characteristic curve), the concept ofconfusion matrix must be understood. When we make a binary prediction, there can be 4 types of outcomes:
To get the confusion matrix, we go over all the predictions made by the model, and count how many times each of those 4 types of outcomes occur:
In this example of a confusion matrix, among the 50 data points that are classified, 45 are correctly classified and the 5 are misclassified.
Since to compare two different models it is often more convenient to have a single metric rather than several ones, we compute two metrics from the confusion matrix, which we will later combine into one:
To combine the FPR and the TPR into one single metric, we first compute the two former metrics with many different threshold (for example
The following figure shows the AUROC graphically:
In this figure, the blue area corresponds to the Area Under the curve of the Receiver Operating Characteristic (AUROC). The dashed line in the diagonal we present the ROC curve of a random predictor: it has an AUROC of 0.5. The random predictor is commonly used as a baseline to see whether the model is useful.
If you want to get some first-hand experience:
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Citation Policy:
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Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.