What is a good accuracy for a CNN?

Building CNN Model with 95\% Accuracy | Convolutional Neural Networks.

Is 80\% 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.

Why is train accuracy higher than test accuracy?

Typically you should have test accuracy less than of the train accuracy. Test data is data unseen by your model, and train data is the data your model use to train itself. So I would say it is more likely luck that you have test accuracy higher than train accuracy.

How much data as percentage should we give to training and testing?

for very large datasets, 80/20\% to 90/10\% should be fine; however, for small dimensional datasets, you might want to use something like 60/40\% to 70/30\%.

How do I get high accuracy on CNN?

Train with more data: Train with more data helps to increase accuracy of mode. Large training data may avoid the overfitting problem. In CNN we can use data augmentation to increase the size of training set.

How do I improve CNN accuracy?

Increase the Accuracy of Your CNN by Following These 5 Tips I Learned From the Kaggle Community

1. Use bigger pre-trained models.
2. Use K-Fold Cross Optimization.
3. Use CutMix to augment your images.
4. Use MixUp to augment your images.
5. Using Ensemble learning.

Why is accuracy a bad measure?

As data contain 90\% Landed Safely. So, accuracy does not holds good for imbalanced data. In business scenarios, most data won’t be balanced and so accuracy becomes poor measure of evaluation for our classification model. Precision :The ratio of correct positive predictions to the total predicted positives.

What is a good 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.

Why is my training accuracy so low?

If the training accuracy is low, it means that you are doing underfitting (high bias). Some things that you might try (maybe in order): Increase the model capacity. Add more layers, add more neurons, play with better architectures.

Why test accuracy is low?

A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specalized to the structure in the training dataset.

Why 70/30 or 80/20 relation between training and testing sets a pedagogical explanation?

When learning a dependence from data, to avoid overfitting, it is important to divide the data into the training set and the testing set. Empirical studies show that the best results are obtained if we use 20-30\% of the data for testing, and the remaining 70-80\% of the data for training.

Why do you split data into training and test sets?

The reason is that when the dataset is split into train and test sets, there will not be enough data in the training dataset for the model to learn an effective mapping of inputs to outputs. There will also not be enough data in the test set to effectively evaluate the model performance.

Is 80\% accuracy good for a machine learning model?

If the second case is happening then accuracy will fall apart in testing data which is not good. However, if the accuracy (validation data) is around 80\% accuracy (training data) given that data points in the validation data are somewhat challenging to the model then we can term it as a good model.

What is the difference between training accuracy and validation accuracy?

Validation accuracy will be usually less than training accuracy because training data is something with which the model is already familiar with and validation data is a collection of new data points which is new to the model. So obviously when the model is interacting with validation data, accuracy will be less than that of training data.

Is 100 samples per class enough to train a CNN?

Your very small data size (100 samples per class) is not likely to be sufficient to train a CNN, and even more so an entire matrix to multiply the data. I would consider doing 2-3 CNN layers with max-pooling, followed by a single fully connected layer to reduce the dimension to that of the output (i.e. number of classes), and see how it goes.

How is the data split between training and test sets?

We apportion the data into training and test sets, with an 80-20 split. After training, the model achieves 99\% precision on both the training set and the test set.

https://www.youtube.com/watch?v=vEgyJlXYxXg