Have you ever delved into the mysterious world of training machine learning models? 🤔 Let’s explore the captivating realm of learning curves and model evaluation, where it’s not just about making predictions but truly understanding how our models learn! 🧠💡
🖥️ Attention all coders! Dive deeper into the process by checking out the full Colab notebook [Google Colab Notebook].
The Set-Up: Unraveling Google Stock Prices with Moving Averages 📈
Picture this: predicting Google’s stock prices using the 50-day and 200-day Simple Moving Averages (SMAs) as our technical indicators, armed with data spanning 2010 to 2024 (now that’s a data goldmine! 🔢). But hold on, it gets even more riveting…
It’s not a one-model show here. Oh no, we’re putting three different powerhouses to the test:
1. Linear Regression (the timeless classic! 🏛️)
2. Support Vector Regression (SVR — the agile contender 🤸♂️)
3. Kernel Ridge Regression (the epitome of synthesis? 🌉)
The Twist: Unveiling Learning Curves 🎭
Here’s where the plot thickens as we reveal the learning curves…