-
Hello,
I think having bindings to R is a good way to advance the OCaml ecosystem
in the ML field.
Seeing a working example of ocaml-r calling functions from the R packages
ranger (random forests…
-
** types. of algorithms.
1. Linear discriminant analysis
2. Regression
3. Naive Bayes
4. Support vector machines
5. Classification and regression trees
6. Random forests
7. Boosting
etc.
-
Prediction of quantiles for a few thousand new records (3000 rows, 3 quantiles, 41 predictors) using a `RandomForestQuantileRegressor` (e.g. `n_estimators=50, min_samples_split=10, min_samples_leaf=10…
-
Hi,
I was wondering if you could detail the significance of the two parameters in the code. The first is "numRandomTests". Is this parameter analogous to mtry in Brieman's random forest (the number o…
-
There are some groups of models:
1. Linear (Lasso, Linear, Ridge, Elastic)
2. Boostings (XGBoost, CatBoost, LGBM, Sklearn boosting)
3. Forests (Random, ExtraTrees)
4. and so on
Each group can b…
-
Suggested list of courses would be:
- An introduction to deep learning **
- How to train a neural network
- Regularisation in neural networks
- Deep Bayesian neural networks
- Conv…
-
I've trained a model using RandomForestLearner and now I need to save it to a TensorFlow saved model, so I can convert to a TFLite model, but when I try to do this, the code returns a AttributeError: …
-
Hi,
thank you for your work on this library.
Other implementations of random forests apply feature bagging at every split during tree generation. So I think it would be better to implement it a…
-
I understand that the sklearn random forest models prediction probabilities are not calibrated and we need to add steps in between to calibrate it.
Just wanted to understand if the prediction proba…
-
We started to discuss the design and implementation of RF-NN in #22, but thought it would be better to track in a separate issue. Because RF-NN is quite different than all other estimators in this pa…