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---
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.
<details>
<summary> Click to expand </summary>
| 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 | |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-text-repr-fallback {display: none;}</style><div id="sk-f64fd6a0-a686-4957-adf1-8209c466f428" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e5865982-53b6-475b-9bbf-6ee40514c813" type="checkbox" checked><label for="e5865982-53b6-475b-9bbf-6ee40514c813" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>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)</pre></div></div></div></div></div>
## 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.
```python
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]
``` |