metadata
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: MLstructureMining_model.bin
Model description
MLStructureMining is a tree-based machine learning classifier designed to rapidly match X-ray pair distribution function (PDF) data to prototype patterns from a large database of crystal structures, providing real-time structure characterization by screening vast quantities of data in seconds.
Intended uses & limitations
[More Information Needed]
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|---|---|
| objective | binary:logistic |
| use_label_encoder | True |
| base_score | 0.5 |
| booster | gbtree |
| colsample_bylevel | 1 |
| colsample_bynode | 1 |
| colsample_bytree | 1 |
| enable_categorical | False |
| gamma | 0 |
| gpu_id | -1 |
| importance_type | |
| interaction_constraints | |
| learning_rate | 0.300000012 |
| max_delta_step | 0 |
| max_depth | 6 |
| min_child_weight | 1 |
| missing | nan |
| monotone_constraints | () |
| n_estimators | 100 |
| n_jobs | 8 |
| num_parallel_tree | 1 |
| predictor | auto |
| random_state | 0 |
| reg_alpha | 0 |
| reg_lambda | 1 |
| scale_pos_weight | |
| subsample | 1 |
| tree_method | auto |
| validate_parameters | 1 |
| verbosity |
Model Plot
The model plot is below.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='', learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor='auto', random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method='auto', validate_parameters=1, verbosity=None)Please rerun this cell to show the HTML repr or trust the notebook.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='', learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor='auto', random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method='auto', validate_parameters=1, verbosity=None)
Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|---|
How to Get Started with the Model
Use the code below to get started with the model.
import xgboost as xgb
import pandas as pd
N_CPU = 8 # Number of CPUs used
# Load model
bst = xgb.Booster({'nthread': N_CPU})
bst.load_model("MLstructureMining_model.bin")
# Load your data
# data = pd.read_csv("your_data.csv")
# data_xgb = xgb.DMatrix(data)
# Do inference
pred = bst.predict(data_xgb)
Model Card Authors
This model card is written by following authors: Emil T. S. Kjær
Model Card Contact
You can contact the model card authors through following channels: emil.thyge.kjæ[email protected]
Citation
Below you can find information related to citation. In review.
BibTeX:
[More Information Needed]