How to choose the right algorithm
1) First you need to consider your goal. What are you trying to get out of this? What data you have or can you collect.
2) If you’re trying to predict or forecast a target value, then you need to look into supervised learning.
3) If not, then unsupervised learning is the place you want to be.
4) If you’ve chosen supervised learning, what’s your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C, or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999, or +infinty to -infinty, then you need to look into regression.
5) If you’re not trying to predict a target value, then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that’s all you need, you should look into clustering.
6) Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes, then you probably should look into a density estimation algorithm.
The rules I’ve given here should point you in the right direction but are not unbreakable laws. You should spend some time getting to know your data, and the more you know about it, the better you’ll be able to build a successful application.
1) First you need to consider your goal. What are you trying to get out of this? What data you have or can you collect.
2) If you’re trying to predict or forecast a target value, then you need to look into supervised learning.
3) If not, then unsupervised learning is the place you want to be.
4) If you’ve chosen supervised learning, what’s your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C, or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999, or +infinty to -infinty, then you need to look into regression.
5) If you’re not trying to predict a target value, then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that’s all you need, you should look into clustering.
6) Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes, then you probably should look into a density estimation algorithm.
The rules I’ve given here should point you in the right direction but are not unbreakable laws. You should spend some time getting to know your data, and the more you know about it, the better you’ll be able to build a successful application.
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