I just created my first working RandomForest classification ml model. It works amazingly well no error and accuracy is 100%.
Is 70% a good accuracy?
If your ‘X’ value is between 70% and 80%, you’ve got a good model. If your ‘X’ value is between 80% and 90%, you have an excellent model. If your ‘X’ value is between 90% and 100%, it’s a probably an overfitting case.
What is an acceptable accuracy score?
If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound. All predictive modeling problems have prediction error.
Is 85% a good accuracy?
In the ubiquitous computing community, there is an unofficial standard that 85% accuracy is “good enough” for sensing based on machine learning. But it’s not so simple to say that 85% should be your target accuracy to consider a system useful.
Why do I get 100% accuracy?
You are getting 100% accuracy because you are using a part of training data for testing. At the time of training, decision tree gained the knowledge about that data, and now if you give same data to predict it will give exactly same value. That’s why decision tree producing correct results every time.
Why do random forests not Overfit?
Random Forests do not overfit. The testing performance of Random Forests does not decrease (due to overfitting) as the number of trees increases. Hence after certain number of trees the performance tend to stay in a certain value.
Why accuracy is not good measure?
We can’t directly say accuracy is poor measure to evaluate. When the data is balanced accuracy is a good measure of evaluating our model. In other hand if data is imbalanced then accuracy is not a correct measure of evaluation. So, accuracy does not holds good for imbalanced data.
What is a good accuracy number?
Accuracy comes out to 0.91, or 91% (91 correct predictions out of 100 total examples). While 91% accuracy may seem good at first glance, another tumor-classifier model that always predicts benign would achieve the exact same accuracy (91/100 correct predictions) on our examples.
How do you interpret an F score?
If you get a large f value (one that is bigger than the F critical value found in a table), it means something is significant, while a small p value means all your results are significant. The F statistic just compares the joint effect of all the variables together.
What is a good prediction accuracy?
If you devide that range equally the range between 100-87.5% would mean very good, 87.5-75% would mean good, 75-62.5% would mean satisfactory, and 62.5-50% bad. Actually, I consider values between 100-95% as very good, 95%-85% as good, 85%-70% as satisfactory, 70-50% as “needs to be improved”.
What is classification accuracy?
Classification accuracy is simply the rate of correct classifications, either for an independent test set, or using some variation of the cross-validation idea.
Do random forests Overfit?
Why is 100% accuracy on test data is not good?
Your test has only a few instances or it is unique. The test is repeated from the train. If the prediction is right and you have 100% accuracy, then no need to do Machine Learning. Open the model find where is taking the decision and don’t do machine learning, do classical modeling.
How is the accuracy of the covid-19 test?
The researchers found that people without COVID-19 symptoms correctly tested positive in 58.1 percent of rapid tests. The 95 percent confidence intervals were 40.2 to 74.1 percent. Accuracy during the first week of symptoms versus the second
Can a rapid test be as accurate as PCR?
Although rapid tests can provide quick results, they aren’t as accurate as PCR tests analyzed in a lab. Keep reading to learn how accurate rapid tests are and when they’re used instead of PCR tests.
