Annealing is a process by which the properties of steel are enhanced to meet machinability requirements. Annealing is a process of heating the steel slightly above the critical temperature of steel (723 degrees Centigrade) and allowing it to cool down very slowly. There are 5 types of annealing: Full Annealing, Process Annealing, Stress Relief Annealing, Spherodise Annealing, and Isothermal Annealing.
The following are some of the advantages of annealing.
· It softens the steel.
· It enhances and improves the machinability of steel.
· It increases the ductility of steel
· It enhances the toughness of steel
· It improves the homogeneity in steel
· The grain size of the steel is refined a lot by annealing
· It prepares the steel for further heat treatment.
With the following data, accurate classify the steel
annealing with the attributes given
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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