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# tft.scale_to_z_score

Returns a standardized column with mean 0 and variance 1.

Scaling to z-score subtracts out the mean and divides by standard deviation. Note that the standard deviation computed here is based on the biased variance (0 delta degrees of freedom), as computed by analyzers.var.

`x` A numeric `Tensor` or `SparseTensor`.
`elementwise` If true, scales each element of the tensor independently; otherwise uses the mean and variance of the whole tensor.
`name` (Optional) A name for this operation.
`output_dtype` (Optional) If not None, casts the output tensor to this type.

A `Tensor` or `SparseTensor` containing the input column scaled to mean 0 and variance 1 (standard deviation 1), given by: (x - mean(x)) / std_dev(x). If `x` is floating point, the mean will have the same type as `x`. If `x` is integral, the output is cast to tf.float32. If the analysis dataset is empty or contains a single distinct value, then the input is returned without scaling.

Note that TFLearn generally permits only tf.int64 and tf.float32, so casting this scaler's output may be necessary.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]