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NASA Asteroid Classification

Discovered on 10 December 07:00 PM EST.

- The train set(generated as per my code) contains 3749 data instances and has 610 instances labeled as 1(hazardous), which means that if a model predicts all values as 0, then the accuracy will be 83.72%.
- Moreover, if we look at Specificity, Mathews Correlation Coefficient and False Positive Rate for the test set then these are ‘nan’, meaning the model is broken and has predicted all values as 0(not hazardous).
- The results table for SVC is quite similar to that of Naive Bayes and hence it clearly shows that SVC has also failed(as Specificity, Mathews Correlation Coefficient and False Positive Rate are again ‘nan’).Let us look at the confusion matrix of SVC.
- The accuracy is 99.4% for test set which is great and also the values of Mathews correlation coefficient and F1 Score are almost touching 1 which denotes that the model is almost perfect.

Read full article on
towardsdatascience.com.

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NASA Asteroid Classification

Discovered on 10 December 05:00 PM EST.

- The train set(generated as per my code) contains 3749 data instances and has 610 instances labeled as 1(hazardous), which means that if a model predicts all values as 0, then the accuracy will be 83.72%.
- Moreover, if we look at Specificity, Mathews Correlation Coefficient and False Positive Rate for the test set then these are ‘nan’, meaning the model is broken and has predicted all values as 0(not hazardous).
- The results table for SVC is quite similar to that of Naive Bayes and hence it clearly shows that SVC has also failed(as Specificity, Mathews Correlation Coefficient and False Positive Rate are again ‘nan’).Let us look at the confusion matrix of SVC.
- The accuracy is 99.4% for test set which is great and also the values of Mathews correlation coefficient and F1 Score are almost touching 1 which denotes that the model is almost perfect.

Read full article on
towardsdatascience.com.