Random-Forest-Classifier | A very simple Random Forest Classifier | Machine Learning library

How do you get predictions out of a RandomForestClassifier? Loosely following the latest docs here, my code looks like...

If all categorical labels could be represented in numerical values, does it means that I could use regression models on any classification tasks by encode the categorical labels as number?

I'm recently working on an binary classification problem that have two output type: '0' for positive and '1' for negative. I've used Random-Forest-Classifier to solve this, but I see others use Random-Forest-Regressor for the same problem. After thinking, it makes sense to me -- the final desired output is continuous value, and I could train a regression model to get predicted continuous value which represents the output class.

This make me think about if it's possible to use regression model on other classification tasks. For example:

To classify two images of 'cat' and 'dog', I use LabelEncoder to encode it as 0 and 1, then it becomes an regression problem.

Hope my question is clear, thanks for helping!

In the Mllib version of Random Forest there was a possibility to specify the columns with nominal features (numerical but still categorical variables) with parameter categoricalFeaturesInfo What's about the ML Random Forest? In the user guide there is an example that uses VectorIndexer that converts the categorical features in vector as well, but it's written "Automatically identify categorical features, and index them"

In the other discussion of the same problem I found that numerical indexes are treated as continuous features anyway in random forest, and it's recommended to do one-hot encoding to avoid this, that seems to not make sense in the case of this algorithm, and especially given the official example mentioned above!

I noticed also that when having a lot of categories(>1000) in the categorical column, once they are indexed with StringIndexer, random forest algorithm asks me setting the MaxBin parameter, supposed to be used with continuous features. Does it mean that the features more than number of bins will be treated as continuous, as it's specified in the official example, and so StringIndexer is OK for my categorical column, or does it mean that the whole column with numerical still nominal features will be bucketized with assumption that the variables are continuous?

I'm trying to use Spark 2.3.1 with Java.

I followed examples in the documentation but keep getting poorly described exception when calling .fit(trainingData).

I am trying to compute the optimal C and Gamma for my SVM. When trying to run my script I get this error:

ValueError: Invalid parameter max_features for estimator SVC. Check the list of available parameters withestimator.get_params().keys().

I went through the docs to understand what n_estimators actually means so that I know what values I should fill in there. But it is not totally clear to me. Could someone tell me what this value should be so that I can run my script in order to find the optimal C and gamma?

my code: