The training dataset is used to prepare a model, to train it. We pretend the test dataset is new data where the output values are withheld from the algorithm. We gather predictions from the trained model on the inputs from the test dataset and compare them to the withheld output values of the test set.

The seven characteristics that define data quality are:

Accuracy and Precision
Legitimacy and Validity
Reliability and Consistency
Timeliness and Relevance
Completeness and Comprehensiveness
Availability and Accessibility
Granularity and Uniqueness

On average, 40% of companies said it takes more than a month to deploy an ML model into production, 28% do so in eight to 30 days, while only 14% could do so in seven days or less.

Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models.

A soulful notion of success rests on the actualization of our innate image. Success is simply the completion of a soul step, however unsightly it may be. We have finished what we started when the lesson is learned. What a fear-based culture calls a wonderful opportunity may be fruitless and misguided for the soul.