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Weighted sum: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ===Introduction== In machine learning, a weighted sum is an algorithmic mathematical operation used to combine multiple input values by assigning weights to each. It's fundamental in many machine learning algorithms such as linear regression, neural networks and decision trees; this transformation transforms input data into one single output value which can then be used for prediction or classification purposes. ==Definition of Weight...") |
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Given an input vector x = [x_1, x_2,..., x_n] and a weight vector w = [w_1, w_2,..., w_n], the weighted sum of x with respect to w is defined as: | Given an input vector x = [x_1, x_2,..., x_n] and a weight vector w = [w_1, w_2,..., w_n], the weighted sum of x with respect to w is defined as: | ||
weighted sum = x_1 * w_1 + x_2 * 2_2 +... + x_n * w_n | |||
==How Weighted Sum is Used in Machine Learning== | ==How Weighted Sum is Used in Machine Learning== | ||