Why Random Forest crushed Linear Regression for my IMDb score

A developer's comparison of machine learning models for predicting IMDb scores found that Random Forest significantly outperformed Linear Regression, with a mean absolute error of 0.68 versus 1.42 and an R² score of 0.74 versus 0.31 on the test set. The Random Forest model, implemented using scikit-learn's RandomForestRegressor with 100 estimators and a max depth of 10, handled non-linear relationships and feature interactions better than Linear Regression, which was skewed by outliers and required one-hot encoding for categorical variables.

Why Random Forest crushed Linear Regression for my IMDb score The Setup and the Crash I built this as a real-world exercise in prompt engineering for data cleaning and basic ML deployment. My goal was to see if I could predict a film's score based on metadata. I used a standard scikit-learn pipeline, but I hit a wall during the preprocessing stage. The first issue was a classic ValueError when I tried to fit the Linear Regression model. I hadn't handled the categorical variables like Genre correctly, and the model choked on the strings. ValueError: could not convert string to float: 'Action' I fixed this using one-hot encoding, but then I ran into a performance bug. My dataset had a few extreme outliers—movies with massive budgets but 1-star ratings—and the Linear Regression model was being pulled wildly off course by them. Comparing the Results Since I wanted a deep dive into why one worked better than the other, I tracked a few specific metrics. I can't use a table here, so here is the breakdown of how they performed on the test set: Linear Regression MAE: 1.42 way too high for a 1-10 scale Random Forest MAE: 0.68 much closer to the actual scores Linear Regression R² Score: 0.31 Random Forest R² Score: 0.74 The Random Forest model won because it handles non-linear relationships and interactions between features much better. For example, the interaction between "Director Reputation" and "Genre" is complex; a horror movie might be rated highly for being "scary," whereas a drama is rated for "acting." Linear Regression tries to find a global average, whereas the decision trees in Random Forest can isolate these specific pockets of data. My Practical Tutorial for Implementation If you're trying to replicate this or doing a similar LLM agent project for data analysis, here is the basic logic I used for the Random Forest implementation: 1. Load the IMDb dataset and drop rows with missing values. 2. Encode categorical features using pd.get dummies . 3. Split the data 80/20 using train test split . 4. Initialize the RandomForestRegressor with n estimators=100 and max depth=10 to prevent overfitting. 5. Fit the model and evaluate using mean absolute error . python from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean absolute error Initialize the model rf model = RandomForestRegressor n estimators=100, random state=42 Training rf model.fit X train, y train Prediction predictions = rf model.predict X test print f"MAE: {mean absolute error y test, predictions }" It's a good reminder that "simpler" isn't always "better" if the underlying data distribution is chaotic. Next Who actually has enough VRAM to run Qwen3.8-2. → /en/threads/6061/