Machine Learning # 2 Classification & Data Preprocessing

Veröffentlicht am: 24 Februar 2019
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In machine learning, classification solve the problem of predicting the categories of a given data point. Pre-processing refers to the transformations applied to our data before feeding it to the algorithm. 1 like = 1 AI

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Vocabulary:
📗Binarization is used when we want to convert our numerical value into Boolean values (true / false).
📗Mean removal mean is a common preprocessing technique used in machine learning. It is usually useful to remove the mean of our feature vector so that each feature is centered on zero. We do this in other to remove bias from the features in our feature vector.
📗Scaling: In our feature vector, the value of each feature vector can varied around many random value. So it become important to scale those features so that it is a level playing field for the machine learning algorithm to train on. We don’t want any feature to be artificially large or small because of the nature of measurement.
📗Normalization: We use the process of normalization to modify the value in the feature vector so that we can measure them on a common scale. In machine learning, we use many different forms of normalization. Some of the most common forms of normalization aim to modify the values so that they sum up to one.

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🔗Links:
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