Do you need validation set for random forest?
Yes, out-of-bag performance for a random forest is very similar to cross validation. Essentially what you get is leave-one-out with the surrogate random forests using fewer trees. So if done correctly, you get a slight pessimistic bias.
What will Fastai do if you don’t provide a validation set?
21. What will fastai do if you don’t provide a validation set? It automatically creates a validation set using 20% of the total training data.
Do we always have to use 224 224 pixel images with the cat recognition model?
Do we always have to use 224×224-pixel images with the cat recognition model? A. No, it is mostly to ensure that the image is the same size as the images that were used to train a model and if we are using pre-trained weights. The decision should be based on the needs of the person training the model.
How do you train a random forest classifier?
I first prepared both training and validation data and then added an extra column ‘train’, which takes the value of 1 when the data is training data and 0 when it is validation data. This is the target that the Random Forest Classifier is going to predict.
How to do cross validation with random forest?
Loop on random generation of RF fits, Get RF prediction on the data for prediction Select the model which best fits the “predicted data” (not the calibration data). This Monte carlos is very consuming, Just wondering if there is another way to do cross validation on random Forest ? (ie NOT the hyper-parameter optimization).
How to use random forest with scikit learn?
Am using Random Forest with scikit learn. RF overfits the data and prediction results are bad. The overfit does NOT depend on the parameters of the RF: NBtree, Depth_Tree
How to tell if you have a representative in random forest?
I am using the metric ROC AUC, which is a common metric for classification tasks. If the metric is 1 then you’re predicting perfectly. If the score is 0.5 then you’re as good as the baseline, which is the score that you would get if you always predicted the most common outcome.