I’m new to the Data Science field and I’m trying to apply XGBoost in a table having 5 rows × 46 columns and my last column is my target column. and the error I’m getting is Can anyone help me with the resolution? Answer I think you need to have the class numerotated from 0 to n-1 where n is
Tag: xgboost
entry_point file using XGBoost as a framework in sagemaker
Looking at the following source code taken from here (SDK v2): I wonder where the your_xgboost_abalone_script.py file has to be placed please? So far I used XGBoost as a built-in algorithm from my local machine with similar code (i.e. I span up a training job remotely). Thanks! PS: Looking at this, and source_dir, I wonder if one can upload Python
Random search grid not displaying scoring metric
I want to do a grid search of some few hyperparameters through a XGBClassifier of a binary class, but whenever i run it the score value (roc_auc) is not being display. I read in other question that this can be related to some error in model training but i am not sure which one is in this case. My model
cannot load pickle files for xgboost images of version > 1.2-2 in sagemaker – UnpicklingError
I can train a XGBoost model using Sagemaker images like so: This work for all versions 1.2-2, 1.3-1 and 1.5-1. Unfortunately the following code only works for version 1.2-2: Otherwise I get a: Am I missing something? Is my “pickle loading code wrong”? The version of xgboost is 1.6.0 where I run the pickle code. Answer I found the solution
How to get SHAP values for each class on a multiclass classification problem in python
I have the following dataframe: For which I want to run a classification algorithm in order to predict the 3 classes So I split my dataset into train and test and I run an xgboost Now I would like to get the mean SHAP values for each class, instead of the mean from the absolute SHAP values generated from this
Error with precision_score of XGBoost classifier with RandomizedSearchCV
I’m trying to make a classifier with XGBoost, I fit it with RandomizedSearchCV. Here is the code of my function: When I run the code, I get an error, reported below: When I do the same thing but with GridSearchCV instead of RandomizedSearchCV, the code runs without any problems! Answer It’s not precision_score it’s ‘precision_score’ (with ‘ ‘), like this-
Why are shap values changing every time I call shap.plots.beeswarm?
So here’s my code using shap : Since I just plot three times the same shape values, I’d expect the three plots to be the same. However, it keeps on changing. After some research, it seems that a new value appear at the top at each call, but why ? Is it a bug in shap ? Edit 1 :
XGBoost Regressor cannot fit the model using string data
I’m trying to use XGBoost to predict a one target (one attribute) dataframe. Below my code. I run it on Colab However, the following error is returned: if I change the last line to I get this error: What I’m doing wrong? any clue? Answer XGBoost cannot handle categorical variables, so they need to be encoded before passing to XGBoost
Perform incremental learning of XGBClassifier
After referring to this link I was able to successfully implement incremental learning using XGBoost. I want to build a classifier and need to check the predict probabilities i.e. predict_proba() method. This is not possible if I use XGBoost. While implementing XGBClassifier.fit() instead of XGBoost.train() I am not able to perform incremental learning. The xgb_model parameter of the XGBClassifier.fit() takes
Input data cannot be a list XGBoost
Here is my code. and the error I’m getting is TypeError: Input data can not be a list. The data coming from test_data is a csv with a team name and obs which is a float like this NYY 0.324 Every way to solve it I’ve seen is just to put it in a 2d array like I did –