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Data Modelling - Coggle Diagram
Data Modelling
Regression
Interpreting the intercepts
Rss
R^2
LS estimates
Logistic Regression
Classification
Sensitivity
Specificty
CA
Good predictor?
probability Distributions
RV
CRV
Discrete RV
Expected value
Variance
Sd
independent RV
iid
Models
Bernoulli
Poisson
Binomial
Uniform
Discrete
Continuous
Gaussian
p(y|parm)
Parameter represents the general usage of parameters to control the probability distribution
represents the l
When do we use Probability distributions?
when the Random Variables take continuous data
Sampling
Taking multiple samples from the population with each an equal sample size
vector y=(y1...yn)
Estimators
Estimators estimating the parameter of a distribution at the population level
Different types of estimators
unbiased estimator (bias=0)
Bias and Variance of an estimator
General method of estimation
Maximum Likelihood estimation arg param max{p(y|param)
solve equivalent problem of minimising the neg log lkelihood
derive in terms of param
dL(y|param)/dparm=0 solve for parm
Estimation
Point estimation
Interval estimation
one METHOD of interval estimation= conf interval
Supervised learning
Usupervised learning
Types of Data
Decesion Tree