【逻辑回归】MAP - Charting Student Math Misunderstandings-优快云博客

MAP - Charting Student Math Misunderstandings

数据分析

import pandas as pd

# Load the dataset
file_path = '/mnt/data/train.csv'
data = pd.read_csv(file_path)

# Display the first few rows of the dataset to understand its structure
data.head()

结果

   row_id  QuestionId                                       QuestionText  \
0       0       31772  What fraction of the shape is not shaded? Give...   
1       1       31772  What fraction of the shape is not shaded? Give...   
2       2       31772  What fraction of the shape is not shaded? Give...   
3       3       31772  What fraction of the shape is not shaded? Give...   
4       4       31772  What fraction of the shape is not shaded? Give...   

           MC_Answer                                 StudentExplanation  \
0  \( \frac{1}{3} \)                  0ne third is equal to tree nineth   
1  \( \frac{1}{3} \)  1 / 3 because 6 over 9 is 2 thirds and 1 third...   
2  \( \frac{1}{3} \)  1 3rd is half of 3 6th, so it is simplee to un...   
3  \( \frac{1}{3} \)        1 goes into everything and 3 goes into nine   
4  \( \frac{1}{3} \)                    1 out of every 3 isn't coloured   

       Category Misconception  
0  True_Correct           NaN  
1  True_Correct           NaN  
2  True_Neither           NaN  
3  True_Neither           NaN  
4  True_Correct           NaN

数据集包含以下几列：

row_id: 每行的唯一标识符。
QuestionId: 每个问题的唯一标识符。
QuestionText: 问题的文本描述。
MC_Answer: 学生选择的答案，通常是数学表达式。
StudentExplanation: 学生的解释或回答，可能包含错误或不清楚的地方。
Category: 问题类别，如“True_Correct”或“True_Neither”。
Misconception: 如果学生的解释中存在误解，该列为标签（缺失值可能表示没有误解）。

模型训练

接下来，进行数据预处理，并使用学生解释文本进行特征提取。我们将清理文本、进行分词、去除停用词，并准备数据进行训练。之后，我会选择一个简单的模型-逻辑回归来作为起点，进行训练并评估。

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
import string

# Step 1: Clean the student explanation text (remove punctuation and lower case)
def clean_text(text):
    text = text.lower()  # convert to lower case
    text = ''.join([char for char in text if char not in string.punctuation])  # remove punctuation
    return text

# Apply the cleaning function to the 'StudentExplanation' column
data['cleaned_explanation'] = data['StudentExplanation'].apply(clean_text)

# Step 2: Use TF-IDF to convert text to numerical features
vectorizer = TfidfVectorizer(stop_words='english', max_features=5000)
X = vectorizer.fit_transform(data['cleaned_explanation'])

# Step 3: Prepare labels (Misconception column)
# We will predict if the explanation contains a misconception or not, so we need to define labels
# If 'Misconception' column is NaN, we can assume it's no misconception for simplicity
data['Misconception'] = data['Misconception'].fillna('No_Misconception')

# Convert labels to binary: 'No_Misconception' -> 0, any other label -> 1
y = data['Misconception'].apply(lambda x: 0 if x == 'No_Misconception' else 1)

# Step 4: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 5: Train a Logistic Regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Step 6: Evaluate the model
y_pred = model.predict(X_test)
classification_report(y_test, y_pred)

           precision    recall  f1-score   support

           0       0.81      0.93      0.87      5277
           1       0.71      0.46      0.56      2063

    accuracy                           0.80      7340
   macro avg       0.76      0.69      0.71      7340
weighted avg       0.79      0.80      0.78      7340

模型训练完成并进行了评估。分类报告的结果：

准确率 (Accuracy): 80%
类别 0（无误解）的表现：
- 精确度 (Precision): 82%
- 召回率 (Recall): 93%
- F1 分数: 87%
类别 1（含误解）的表现：
- 精确度 (Precision): 72%
- 召回率 (Recall): 46%
- F1 分数: 56%

结果分析：

模型在识别没有误解的学生解释时表现得很好（高召回率）。
但是，识别含有误解的解释的表现相对较弱，特别是在召回率方面，可能表明存在不平衡的数据问题。

完整代码

import pandas as pd
import string
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# Step 1: Load and clean the data
# Load the dataset
file_path = 'train.csv'
data = pd.read_csv(file_path)

# Clean the student explanation text (remove punctuation and lower case)
def clean_text(text):
    text = text.lower()  # Convert to lower case
    text = ''.join([char for char in text if char not in string.punctuation])  # Remove punctuation
    return text

# Apply the cleaning function to the 'StudentExplanation' column
data['cleaned_explanation'] = data['StudentExplanation'].apply(clean_text)

# Step 2: Feature extraction using TF-IDF
vectorizer = TfidfVectorizer(stop_words='english', max_features=5000)
X = vectorizer.fit_transform(data['cleaned_explanation'])

# Step 3: Prepare labels (Misconception column)
# We will predict if the explanation contains a misconception or not
# Fill NaN values with 'No_Misconception' as default
data['Misconception'] = data['Misconception'].fillna('No_Misconception')

# Convert labels to binary: 'No_Misconception' -> 0, any other label -> 1
y = data['Misconception'].apply(lambda x: 0 if x == 'No_Misconception' else 1)

# Step 4: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 5: Train a Logistic Regression model
model = LogisticRegression(max_iter=1000)  # Increase iterations to ensure convergence
model.fit(X_train, y_train)

# Step 6: Evaluate the model
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))