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Explanation: Min-Max Normalization is a technique used to scale features to a fixed range, typically [0,1]. This transformation is particularly useful for algorithms sensitive to the scale of input data, such as gradient descent-based models. This method ensures that each feature contributes proportionately to the model, eliminating bias caused by varying scales across features. Min-Max Normalization is especially suitable for cases where the data has a defined range, making it ideal for neural networks and distance-based algorithms like k-NN. Option A: Z-score Standardization scales data to have a mean of 0 and a standard deviation of 1, which is more suitable for normally distributed data. It does not confine the values to a specific range like [0,1]. Option C: One-Hot Encoding is used for categorical variables, converting them into binary vectors. It is not applicable for scaling numerical data. Option D: Logarithmic Transformation is used to handle skewness in data and is not designed to scale values into a fixed range. Option E: Ordinal Encoding converts categorical data into integers based on their ordinal rank, which is unrelated to numerical feature scaling.
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A boat can cover 66 km upstream and 165 km downstream in 13 hours. Speed of the stream is how much less than the speed of the boat in still water if the...
The speed of a boat in still water is 200% higher than the speed of stream. If the boat takes 4 hours to cover 96 km downstream, find the time it will t...