gbm | Gradient boosted models | Machine Learning library

Here my data for boosting

I wanted to use the optimal k value to conduct kNN clasification and predict the dependent variable diabetes in test set using train set and compare the results with the real values.

I've already got optimal k value and got the accruacy already. After that, I wanted to compare the results with the real value with using confussionMatrix but I got the problem with the different length.

I've already checked that nrow and length quantities are same(with 74) but it still have same problem.

Could you help me to overcome this problem?

My codes are as like below

I just wanted to conduct a kNN classification with the situation when k is 3. I would like to predict the dependent variable “diabetes” in valid set using train set and calculate the accuracy.

But I faced to the error message with

Error in knn(train = TrainXNormDF, test = ValidXNormDF, cl = MLdata2[, : 'train' and 'class' have different lengths

I can't solve this problem with get approach with

It's my first time using TypeScript and using OOP. I've created an interface for a common brokers methods. (Buy, Sell, sell limit, buy limit)

Then i implemented this interface with my brokers API. GBM, Bitso, IB, AmeriTrade and so on i want to instance the correct class through a received string in my end point

example: "/api/broker/:brokername" so i created the following code:

In NodeJS I'm trying to verify a message was sent by Google before processing the message contents.

This is done by comparing the message header "x-goog-signature" to a generated signature.

The generated signature code takes as input the service account key and the http raw body.

Following this example.

Update: The problem does not occur with the NVIDIA driver version 510 anymore.

I am running Android Studio 2020.3.1 on Wayland and I'm using the proprietary NVIDIA driver version 495, which allows me to use the the regular GBM implementation of Wayland instead of the especially tailored EGL version.

My problem is, that every time I try to start an Android emulator (AVD) via the AVD Manager, I get the following message after a few seconds:

The emulator process for AVD Pixel_2_API_30 has terminated.

Starting the AVD from the command-line shows that a segmentation fault occurred:

Using the h2o package for R, I created a set of base models using AutoML with StackedEnsemble's disabled. Thus, the set of models only contains the base models that AutoML generates by default (GLM, GBM, XGBoost, DeepLearning, and DRF). Using these base models I was able to successfully train a default stacked ensemble manually using the h2o.stackedEnsemble function (i.e., a GLM with default params). I exported the model as a MOJO, shutdown the H2O cluster, restarted R, initialized a new H2O cluster, imported the stacked ensemble MOJO, and successfully generated predictions on a new validation set.

So far so good.

Next, I did the exact same thing following the exact same process, but this time I made one change: I trained the stacked ensemble with all pairwise interactions between the base models. The interactions were created automatically by feeding a list of the base model Ids to the interaction metalearner_parameter. The model appeared to train without issue and (as I described above) was able to export it as a MOJO, restart the h2o cluster, restart R, and import the MOJO. However, when I attempt to generate predictions on the same validation set I used above I get the following error:

Hi everyone based on the wage-dataset (wage being the dependent variable) and on the workflow created below, I would like to find out the following:

1. What is the predicted wage of a person with age equal to 30 for each piecewise model?
2. Considering the flexible pw6_wf_fit model configuration and in particular the six breakpoints above: Exceeding which (approximate) value of age correlates strongest with wage?

I tried to use versions of extract but so far I don´t know how to apply it in R. Helpful for any comment

The code I use is the following:

I have a plot with 5 lines that are dashed, 5 that are solid. This is correctly displaying in the plot. However, using the following code, the legend shows all line to be solid. I find this strange, because the legend is showing the correct variation in the line size, which is formatted in the same way. I tried replacing "Factorlevel" = "solid" by "Factorlevel" = 1, but this didn't work either.

Please see reproducible example below my code