Evaluation Models
                     Click to edit Master title style
EVALUATION MODEL1 S
MCloicdke lt oE veadliuta Mtiaosnter title style
Model Evaluation is an integral part of the model development 
process. It helps to find the best model that represents our 
data and how well the chosen model will work in the future.
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AClBicOkU tTo  Meoddite Ml Eavstaelur attitiolen style
Evaluating model performance with the data used for training is 
not acceptable in data science because it can easily generate 
overoptimistic and overfitted models. 
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CMlEicTkH tOoD eSd OitF M EaVsAteLrU tAitTleIN sGt yMleODELS
There are two methods of evaluating models in data science, 
Hold-Out and Cross-Validation. 
To avoid overfitting, both methods use a test set (not seen by 
the model) to evaluate model performance.
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TClriacikn itnog e sdeit Master title style
Training set is a subset of the dataset used to build predictive 
models.
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CVlailcikd attoi oend iste Mt aster title style
Validation set is a subset of the dataset used to assess the 
performance of model built in the training phase. 
It provides a test platform for fine tuning model's parameters 
and selecting the best-performing model. Not all modeling 
algorithms need a validation set.
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CTleicstk  steot edit Master title style
Test set or unseen examples is a subset of the dataset to 
assess the likely future performance of a model. If a model fit 
to the training set much better than it fits the test set, overfitting 
is probably the cause.
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Clriocsks -tVoa elidiat tMioanster title style
When only a limited amount of data is available, to achieve an 
unbiased estimate of the model performance we use k-fold 
cross-validation. In k-fold cross-validation, we divide the data 
into k subsets of equal size.
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Cloinckfu tsoio end Mit aMtraisxter title style
A confusion matrix shows the number of correct and incorrect 
predictions made by the classification model compared to the 
actual outcomes (target value) in the data.
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Cliacsks itfoic aetdiiotn M Eavsatelura ttiitolen style
Classifiers are commonly evaluated using either a numeric 
metric, such as accuracy, or a graphical representation of 
performance, such as a receiver operating characteristic 
(ROC) curve
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CRleicgrke tsos ieodni tE vMaalustaetrio tnitle style
Regression is a type of Machine learning which helps in finding 
the relationship between independent and dependent variable.
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