CS 420 Lecture Notes - Lecture 3: Support Vector Machine, Machine Learning, Logistic Regression
14 Jun 2019
School
Department
Course
Professor
Document Summary
Steps required in selecting the right machine learning algorithm. Getting the most accurate answer possible isn"t always necessary. Sometimes an approximation is adequate, depending on what you want to use it for. If that"s the case, you may be able to cut your processing time dramatically by sticking with more approximate methods. Another advantage of more approximate methods is that they naturally tend to avoid overfitting. The number of minutes or hours necessary to train a model varies a great deal between algorithms. Training time is often closely tied to accuracy one typically accompanies the other. In addition, some algorithms are more sensitive to the number of data points than others. When time is limited it can drive the choice of algorithm, especially when the data set is large. Lots of machine learning algorithms make use of linearity. Linear classification algorithms assume that classes can be separated by a straight line (or its higher-dimensional analog).
