{"slug": "why-random-forest-crushed-linear-regression-for-my-imdb-score", "title": "Why Random Forest crushed Linear Regression for my IMDb score", "summary": "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.", "body_md": "# Why Random Forest crushed Linear Regression for my IMDb score\n\n## The Setup and the Crash\n\nI 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.\n\nThe first issue was a classic `ValueError`\n\nwhen 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.\n\n```\nValueError: could not convert string to float: 'Action'\n```\n\nI 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.\n\n## Comparing the Results\n\nSince 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:\n\n**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\n\nThe 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.\n\n## My Practical Tutorial for Implementation\n\nIf 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:\n\n1. Load the IMDb dataset and drop rows with missing values.\n\n2. Encode categorical features using `pd.get_dummies()`\n\n.\n\n3. Split the data 80/20 using `train_test_split`\n\n.\n\n4. Initialize the `RandomForestRegressor`\n\nwith `n_estimators=100`\n\nand `max_depth=10`\n\nto prevent overfitting.\n\n5. Fit the model and evaluate using `mean_absolute_error`\n\n.\n\n``` python\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error\n\n# Initialize the model\nrf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n\n# Training\nrf_model.fit(X_train, y_train)\n\n# Prediction\npredictions = rf_model.predict(X_test)\nprint(f\"MAE: {mean_absolute_error(y_test, predictions)}\")\n```\n\nIt's a good reminder that \"simpler\" isn't always \"better\" if the underlying data distribution is chaotic.\n\n[Next Who actually has enough VRAM to run Qwen3.8-2. →](/en/threads/6061/)", "url": "https://wpnews.pro/news/why-random-forest-crushed-linear-regression-for-my-imdb-score", "canonical_source": "https://promptcube3.com/en/threads/6075/", "published_at": "2026-08-12 20:46:19+00:00", "updated_at": "2026-08-12 20:50:16.343616+00:00", "lang": "en", "topics": ["machine-learning", "ai-tools"], "entities": ["scikit-learn", "RandomForestRegressor", "Linear Regression", "IMDb"], "alternates": {"html": "https://wpnews.pro/news/why-random-forest-crushed-linear-regression-for-my-imdb-score", "markdown": "https://wpnews.pro/news/why-random-forest-crushed-linear-regression-for-my-imdb-score.md", "text": "https://wpnews.pro/news/why-random-forest-crushed-linear-regression-for-my-imdb-score.txt", "jsonld": "https://wpnews.pro/news/why-random-forest-crushed-linear-regression-for-my-imdb-score.jsonld"}}