File size: 1,982 Bytes
4b6fcac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # MAP Misconception Detection – Classical Models
This repository contains classical machine learning components used for the **Kaggle MAP (Misconception Annotation Project)** competition. We developed two independent pipelines using **TF-IDF vectorization** and **Logistic Regression** for:
- **Task 1**: Category classification
- **Task 2**: Misconception label prediction
---
## 📁 Included Files
### 🔹 Task 1 (Category Classification)
| File | Description |
|------|-------------|
| `tfidf_task1.pkl` | TF-IDF vectorizer fitted on student answers for Task 1 |
| `logreg_task1.pkl` | Trained logistic regression model for predicting `category` labels |
---
### 🔹 Task 2 (Misconception Detection)
| File | Description |
|------|-------------|
| `tfidf_task2.pkl` | TF-IDF vectorizer fitted on student answers for Task 2 |
| `logreg_task2.pkl` | Trained logistic regression model for predicting misconception labels |
---
### 🔹 Label Encoders
| File | Description |
|------|-------------|
| `label_encoder_category.pkl` | LabelEncoder used to convert `category` labels into integers |
| `label_encoder_miscon.pkl` | LabelEncoder used to convert misconception labels into integers |
---
## 🧠 Usage
To use the models in your code:
```python
import joblib
# Load Task 1 components
tfidf1 = joblib.load("tfidf_task1.pkl")
clf1 = joblib.load("logreg_task1.pkl")
# Load Task 2 components
tfidf2 = joblib.load("tfidf_task2.pkl")
clf2 = joblib.load("logreg_task2.pkl")
# Load label encoders
le_category = joblib.load("label_encoder_category.pkl")
le_miscon = joblib.load("label_encoder_miscon.pkl")
🛠️ Requirements
These models were trained using:
- scikit-learn
- joblib
```bash
pip install scikit-learn joblib
```
📜 Licens:
MIT License. You are free to use, modify, and distribute this model with proper attribution.
✍️ Author:
Godspower Maurice (PythonCreate)
Created for the MAP Math Misconceptions @ Kaggle NLP competition — 2025.
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