Okay, I am going to tell you about my experience on how I featurize my model. It’s a messy process, but I am here to share it, hoping it can help someone out there.
Starting from Scratch
I began with a bunch of raw data. Images, text, you name it. First thing I did was to clean up the mess. Removed duplicates, fixed the typos, and got rid of the stuff that was totally irrelevant. It took days, honestly.
Figuring out the Important Stuff
Then, I had to figure out what features actually matter. I used a few techniques, like correlation analysis and some basic statistical tests. It was a lot of trial and error. I selected features that seemed to have a strong relationship with the output I was interested in.
- Feature Selection: I had to be really picky. Too many features can make things really complicated and slow.
- Trial and Error: It was not easy. I spent a lot of time running experiments and checking the results.
- Keeping it Simple: I focused on the features that made the most sense. If a feature was too complex or did not add much value, I just dropped it.
Making New Features
Sometimes, the data you have is not enough. So, I created new features from the existing ones. Like, if I had dates, I’d extract the day of the week, or the month. For text data, I did things like counting words or checking for specific phrases.
- One-Hot Encoding: I turned categories into numbers. Simple but effective.
- Binning: I grouped values into bins. For example, ages into groups like “0-18”, “19-35”, and so on.
- Splitting: I split strings into parts. Like, splitting a full name into first and last names.
Testing and More Testing
After all that, I had to test everything. I split my data into training and testing sets, built my models, and checked the results. It was a cycle of building, testing, and tweaking. I kept doing this until I was happy with the performance.
In the end, I managed to build a model that worked pretty well. It was a long process, but I learned a lot. I hope sharing my experience helps someone out there. Remember, it is okay to mess up and try again. That is how we learn. And hey, if you have any questions, feel free to ask. I might not have all the answers, but I will try my best to help.
Original article by the Author:Simo,If you intend to republish this content, please attribute the source accordingly:https://www.suntrekenergy.com/5715.html
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