great performance hard mineral classifier

CLASSIFICATION // CHARACTERIZATION OF SOME ROCK

A. Isotropic, hard rocks Hornfels, some basalts B. Anisotropic on a macro scale, but microscopically isotropic hard rocks Cemented shales, flagstones C.Microscopically anisotropic hard rocks Slate, phyllite D.Soft, soil like rocks Compaction shale, chalk, marl

The Model Performance Mismatch Problem (and what to

Actually, the model performs quite well, reaching 0.99 F1 score on the test data. The confusion matrix about the performance of the model on the test data is: 384 3 2 223 However, when I use the trained classifier to make the prediction on all the tweets I have collected. The performance is really bad.

A good Machine Learning classifiers accuracy metric for ...

Tour of Evaluation Metrics for Imbalanced Classification

01/05/2021· A no skill classifier will have a score of 0.5, whereas a perfect classifier will have a score of 1.0. ROC AUC = ROC Area Under Curve Although generally effective, the ROC Curve and ROC AUC can be optimistic under a severe class imbalance, especially when the number of examples in the minority class is small.

Performance Measures for Multi Class Problems Data ...

04/12/2018· With a value of 0.88, F 1 m i c r o is quite high, which indicates a good overall performance. As expected, the micro averaged F1, did not really consider that the classifier had a poor performance for class E because there are only 5

Types of Minerals Definition, Classification Examples ...

Each of these minerals is different yet many times minerals look like one another or something else. A piece of green coloured plastic may look identical to an emerald. A very clear piece of quartz may look like a rough diamond. The Mols hardness test a streak test, colour, luter, cleavage and fracture are all ways of identifying minerals.

Performance Metrics for Machine Learning Models by ...

Drillability Assessments in Hard Rock

11/10/2017· Large investments and high geological risk are involved using TBMs, and disc cutter consumption has a great influence on performance and cost, especially in hard rock conditions.

How to Evaluate Machine Learning Model Performance in ...

15/05/2020· There is a very simple trick to handle then model performance when AUC gets in the range of 0, 0.5. Trick: Simply switch the class labels predicted by the model. Yes, suppose we get AUC of 0.32 then after switching the class labels 0 to 1 and 1 to 0, we get an AUC value of 10.32 = 0.68 which makes it a good model. Its that simple!