Although, there is not a simple answer for what came first - Chicken or Egg?, in Machine Learning, there is an easy answer. Data came first before any function. Machine learning is all about learning from data. The learning algorithm tries to learn a function that can either classify the data into different categories, or learn the function itself that plots the data.
There are two popular ways in which a Machine Learning algorithm can be taught to learn a function, but in both cases it needs data.
- Supervised Learning: We give the algorithm a lot of data with both input and output, and it learns the function. In case of regression problems, the function approximately plots the function that understands the data. In case of classification problems, the function tries to classify the data.
- Unsupervised Learning: We give the algorithm a lot of input data with no output, and it tries to find patterns in the data. The algorithm classifies the data based on the similarities of the data points. This method is called clustering.
- Training Data: This data is used by the algorithm to learn the function, based on which it tries to generalize.
- Validation Data: There is a high chance that the algorithm might overfit the training data, and will fail on any other data. To protect it from doing so, validation data is used. Validation data helps the algorithm to correlate how accurate it works on both known and unknown data.
- Test Data: Once the function is learnt, it is tested on the test data. Here the algorithm checks whether it is also able to generalize on the test data, and thus able to stand a better chance in generalizing future data it has to do inference on.
Two interesting questions emerge, both having elegant answers.
Question. What are the parameters to decide on the amount of data needed for the algorithm to learn a function?
Answer. This might come with experience, but the more the merrier. Today’s Machine Learning algorithms such as Neural Networks are so powerful that if not enough data is given, they overfit easily. Also once your model is fine tuned and no more optimization is possible, it will only do better with more data.
Question. What if there are outliers in the data?
Answer. To get better performance, such outliers should be filtered from the data, otherwise the algorithm might get confused, and thus create a function that tries to learn the outlier too. It also depends on how sensitive some algorithms are to such outliers.
In today’s generation where companies having more data are richer than companies with less data, it seems data will decide the future.
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