Machine Learning

gistfile1.txt

Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

Linear Regression= Straigh line draw by the machine using set of data points
Polynomial regression = Non linear data points, curved line to fit better

Linear logistic regression = spam example, two different groups


Supervised learning = Train the model by giving it examples and guiding it to the right answers.

Accurancy = Given data that we already knows how many % the model answer right.

Precision = Use false positives to determine how precise the model is. The number of true positives, divided by the total number of positives

*Article definitions:*
Quantitative models: These are machine learning models that answer questions with numeric answers, e.g. 150cm, 42 days, 20 cars, etc.

Qualitative models: These are machine learning models that answer questions with answers that are not numeric, e.g. “Yes and No”, “Rainy, Snowy, or Sunny”, “Happy, Sad, Excited, or Angry”, etc.

Accuracy: The accuracy of a model describes how often the model answers the question correctly.

Supervised learning: This is when we train a model using data where we already know what the answer should be so that we can guide the model in its training.

Precision: The precision of a model describes how many of the models “Yes” answers were actually correct.

Recall: The recall of a model describes how many of the answers that should be “Yes” the model got correct.