Standardization (or Z-score normalization) is a linear transformation that centers the data around zero and scales it to have a standard deviation of one. It subtracts the mean of the feature from each data point and divides by the standard deviation.
Conditions: Applied to a continuous numerical feature.; Requires knowing the mean and standard deviation of the feature.; The standard deviation must not be zero (to avoid division by zero).
Standardization (or Z-score normalization) is a linear transformation that centers the data around zero and scales it to have a standard deviation of one. It subtracts the mean of the feature from each data point and divides by the standard deviation.
Conditions: Applied to a continuous numerical feature.; Requires knowing the mean and standard deviation of the feature.; The standard deviation must not be zero (to avoid division by zero).
Gradient descent is an optimization algorithm used to minimize a cost function by iteratively updating the model parameters in the opposite direction of the gradient of the cost function with respect to those parameters.
Conditions: The cost function J must be differentiable with respect to the parameters θ.; The learning rate α must be chosen appropriately to ensure convergence.
Gradient descent is an optimization algorithm used to minimize a cost function by iteratively updating the model parameters in the opposite direction of the gradient of the cost function with respect to those parameters.
Conditions: The cost function J must be differentiable with respect to the parameters θ.; The learning rate α must be chosen appropriately to ensure convergence.
Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).
Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).
Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).
Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).