(3.5) Analyze the results
This is part of a series of articles called Azure Challenges. You can refer to the Intro Page to understand more about how the challenges work.
When your code is done you can start analyzing the results…
Data guardrails help you identify potential issues with your data (for example, missing values or class imbalance). They also help you take corrective actions for improved results.
Models
Spearman’s rank correlation coefficient
In statistics, Spearman’s rank correlation coefficient or Spearman’s ρ, named after Charles Spearman and often denoted by the Greek letter, is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables). It assesses how well the relationship between two variables can be described using a monotonic function.
The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson’s correlation assesses linear relationships, Spearman’s correlation assesses monotonic relationships (whether linear or not). If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other.
Intuitively, the Spearman correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) rank (i.e. relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully opposed for a correlation of −1) rank between the two variables.
Spearman’s coefficient is appropriate for both continuous and discrete ordinal variables.
Both Spearman’s and Kendall’s can be formulated as special cases of a more general correlation coefficient.
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Child Runs
Automated ML experiment child runs can be performed on a cluster that is already running another experiment. However, the timing depends on how many nodes the cluster has, and if those nodes are available to run a different experiment.
Each node in the cluster acts as an individual virtual machine (VM) that can accomplish a single training run; for automated ML this means a child run. If all the nodes are busy, a new experiment is queued. But if there are free nodes, the new experiment will run automated ML child runs in parallel in the available nodes/VMs.
Snapshots
Models
Meteics
Data Transformation Steps
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