: Check if the data is properly divided into training, validation, and test sets to ensure the model's reliability on new data.
: Converting text into numerical data using techniques like TfidfVectorizer or CountVectorizer . svc.py
: Using sklearn.svm.SVC for classification. : Check if the data is properly divided
: Importing data (e.g., from CSV or JSON) and cleaning text by removing stop words and handling n-grams to improve accuracy. svc.py
: Generating reports to check for overfitting (requires reducing polynomial degree) or underfitting (requires increasing degree). Key Areas to Check During Your Review
A well-structured svc.py usually includes the following stages:
: Ensure the model uses class_weight='balanced' if your dataset has an uneven number of positive and negative samples.