The machine learning research community is very and very vibrant.
Here is what I mean...🧵🧵
In 1958, Frank Rosenblatt invented a perceptron, a very simple algorithm that would later turn out to be the core and origin of to days intelligent machines.
In essence, the perceptron is a simple binary classifier that can determine whether or not a given input belongs to a specific class.
Precision: What is the percentage of positive predictions that are actually positive?
Recall: What is the percentage of actual positives that were predicted correctly?
The fewer false positives, the higher the precision. Vice-versa.
The fewer false negatives, the higher the recall. Vice-versa.
How do you increase precision? Reduce false positives.
It can depend on the problem, but generally, that might mean fixing the labels of those negative samples(being predicted as positives) or adding more of them in the training data.
◆A quick look into the dataset
◆Summary statistics
◆Finding the basic information about the dataset
◆Checking missing data
◆Checking feature correlations