CS代考程序代写 decision tree LECTURE 3 TERM 2:

LECTURE 3 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE

END-TO-END ML
— Discover — Explore — Visualize
— Clean
— Sample — Impute — Encode — Transform
– Scale
— Modeling
– Overfitting
– Optimization
– ModelSelection – Regularization
– Generalization
— Documentation — Presentation
— Launch — Monitor — Maintain

DECISION BOUNDARY SOFTMAX REGRESSION

DECISION TREES

DECISION TREES

DECISION TREES
Classification Trees

DECISION TREE (IRIS DATA)

DECISION TREE BOUNDARIES

DECISION TREE BOUNDARIES

COST FUNCTIONS
%
𝐺! =1−%𝑝!,”‘ “#$
𝐽 𝑘, 𝑡” = 𝑚()*+ 𝐺()*+ + 𝑚,!-.+ 𝐺,!-.+ 𝑚𝑚

REGUL ARIZ ATION

RIDGE (REGULARIZED) REGRESSION

REGUL ARIZ ATION
• k – features
• tk – thresholds
• min_samples_split • min_samples_leaf • max_leaf_nodes
• max_features

DECISION TREES
Regression Trees

REGRESSION TREES

TREE REGRESSIONS
𝐽 𝑘, 𝑡! = 𝑚”#$% 𝑀𝑆𝐸”#$% + 𝑚&'()% 𝑀𝑆𝐸&'()% 𝑚𝑚

TREE REGRESSIONS

REGULARIZING A TREE REGRESSOR

INSTABILITY

SENSITIVITY TO TRAINING SET

Source: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

QUES TIONS
— If a Decision Tree is overfitting the training set, is it a good idea to try decreasing max_depth?
— If a Decision Tree is underfitting the training set, is it a good idea to try scaling the input features?

LECTURE 3 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE