Why test accuracy is more than training accuracy?

Why test accuracy is more than training accuracy?

Test accuracy should not be higher than train since the model is optimized for the latter. Ways in which this behavior might happen: you did not use the same source dataset for test. You should do a proper train/test split in which both of them have the same underlying distribution.

How do you increase test data accuracy?

8 Methods to Boost the Accuracy of a Model

1. Add more data. Having more data is always a good idea.
2. Treat missing and Outlier values.
3. Feature Engineering.
4. Feature Selection.
5. Multiple algorithms.
6. Algorithm Tuning.
7. Ensemble methods.

Which is better, training accuracy or test accuracy?

Imagine if you’re using 99% of the data to train, and 1% for test, then obviously testing set accuracy will be better than the testing set, 99 times out of 100. The solution here is to use 50% of the data to train on, and 50% to evaluate the model. Accuracy on the training set might be noise, depending on which ML algorithm you are using.

Which is better validation data or training data?

Even though the data is under fitted, the validation data may perform well under circumstances that the validation data fits better in your model than does training data. Increase more convolution layers and loosen up on Dropout layers, lesser dropout layers or lower percentage of units dropped out.

Why is ML accuracy higher than training set?

Accuracy on the training set might be noise, depending on which ML algorithm you are using. The training set accuracy doesn’t evaluate the correctness of your model on unseen rows. One strategy is to ignore the training set accuracy.

Why is my are squared different from my training data?

One explanation might relate to how you subset your test data (they way you split training and testing data). If your test data only consists of (just a few) similar observations then it is very likely for your R-squared measure to be different than that of the training data.