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easy15 ptsmachine-learningstatistics

Min-Max Feature Scaling

Before training, a pipeline rescales a numeric feature so that its smallest value becomes 0 and its largest becomes 1: each value v becomes (v - min) / (max - min).

Round every result to 4 decimal places with Math.round(x * 10000) / 10000. If all values are equal (including a single value), return all zeros. An empty input returns [].

Example 1

Input: values = [10,20,30]
Output: [0,0.5,1]
Explanation: min 10, max 30, so 20 maps to 0.5.

Example 2

Input: values = [3,1,2,4]
Output: [0.6667,0,0.3333,1]

Constraints

  • 0 <= values.length <= 10^5
  • -10^9 <= values[i] <= 10^9
Hint 1

Find min and max in one pass, then map.

Hint 2

Guard against dividing by zero when max equals min.

Good practice for: Data Scientist, Machine Learning Engineer, AI Engineer (LLM & Generative AI), MLOps Engineer, Data Engineer

Run checks your code on 3 sample tests. Submit runs all 8 tests, including hidden ones, and earns points. Use console.log to debug. Python's first run can take a few seconds while it starts.