random-forest | Randomized Decision Trees : A Fast C Implementation | Machine Learning library

I was trying to improve my random forest classifier parameters, but the output I was getting, does not look like the output I expected after looking at some examples from other people.

The code I'm using:

The RMSE values in my MLP and LSTM model seems to change when tested on the same sample and model again and again. I found this question, where adding a random state solved the issue. Is there something like that I could do too?

Sharing my MLP code here:

I used this site as a reference https://www.r-bloggers.com/2021/02/how-to-build-a-handwritten-digit-classifier-with-r-and-random-forests/

to write a handwritten digit classifier using R with random forests.

Is it possible to build a plot of the colMeans obtained at the end of the code? The MNIST train and test datasets (that you can find in the link above) don't have any column headings. I'm new to R and still learning. Any kind of help would be greatly appreciated.

Here's the code:

I am trying to fit a RandomForestClassifier, like this.

I'm trying to build A conditional random field model, following this tutorial https://www.kaggle.com/shoumikgoswami/ner-using-random-forest-and-crf I have followed all the steps but for some reason when I run the line

I have been following along with this really helpful XGBoost tutorial on Medium (code used towards bottom of article): https://medium.com/analytics-vidhya/random-forest-and-xgboost-on-amazon-sagemaker-and-aws-lambda-29abd9467795.

To-date, I've been able to get data appropriately formatted for ML purposes, a model created based on training data, and then test data fed through the model to give useful results.

Whenever I leave and come back to work more on the model or feed in new test data however, I find I need to re-run all model creation steps in order to make any further predictions. Instead I would like to just call my already created model endpoint based on the Image_URI and feed in new data.

Current steps performed:

Model Training

The same question has been asked. But since the OP didn't post the code, not much helpful information was given.

I'm having basically the same problem, where for some reason shuffling data is making a big accuracy gain (from 45% to 94%!) to my random forest classifier. (In my case removing duplicates also affected the accuracy, but that may be a discussion for another day) Based on my understanding on how RF algorithm works, this really should not happen.

My data are merged from several files, each containing the same samples in the same order. For each sample, the first 3 columns are separate outputs, but currently I'm just focusing on the first output.

The merged data looks like this. The output (1st column) is ordered and unevenly distributed:

The shuffled data looks like this:

I'm referring to this Random Forrest Algorithm example to predict rejection in different stages.

I'm fetching values from the database for stages and reject_count. And using stages values for x and reject_count values for y. My code is:

I was reading a blog about Feature Selection based on the density curves of the features. The blog is in R language and I am not familiar with that.

Blog:
- https://myabakhova.blogspot.com/2016/02/computing-ratio-of-areas.html
- https://www.datasciencecentral.com/profiles/blogs/choosing-features-for-random-forests-algorithm

The blog says if the density curves of two features are significantly different (look below the equation, which says > 0.75), then we can discard one of the features.

Now, I am familiar with how to plot density curves, but not sure how to get the intersection area. Any help with finding the intersection area is greatly appreciated.

Here is my attempt: