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Python with AI – 2
Session 7
Plots recap
• Histograms
Plots recap
• Histograms
• Bar Plot
Plots recap
• Histograms
• Bar Plots
• Scatter Plots
Plots recap
• Histograms
• Bar Plots
• Scatter Plots
• Line Plots – Another type of plot that we will use today
Line Plots
Line Plots
Line Plots
Line Plots
• Specify both x and y axis
REST API recap
json.dumps() converts
dictionary to striing
Convert the output you
receive into readable format
REST API output
{"statusCode": 200,
"body": "{"predicted_label": "sad", "Insight": {},
"score": [[0.004525359954210217, 0.9954746400457898]],
"Message": "Predictions were made on the column: feeling",
"Model": "MLPClassifier-91bcae7b-9b40-4cfa-b9cc-905d377cf12b"}",
"headers": {"Content-Type": "application/json",
"Access-Control-Allow-Origin": "*"}}
Model - analysis
• Confusion Matrix
• Precision
• Recall
• False Positive Rate
• True positive rate =
Recall = Sensitivity
ROC curve
ROC curve
Threshold False Positive Rate True Positive Rate
0.2
0.4
0.6
0.8
0.9
ROC analysis
• ROC curves are graphical representations of the results from testing
an AI
• They are used to determine trade-off between sensitivity and
specificity
• Area under the curve of the ROC tells us how good the performance
of the AI is.
Regression
• Root Mean Square Error (RMSE)
• Take the difference (Prediction – Right answer)
• Square it so that it is always positive (Prediction – Right Answer) *
(Prediction – Right Answer)
• Take the average (Mean)
• Take the square root
Regression
• Root Mean Square Error (RMSE)
• Mean Square Error (MSE)
• R2 score ?
Regression
• Root Mean Square Error (RMSE)
• Mean Square Error (MSE)
• R2 score
• Unexplained variance =sum(SE)
• Average value of label = A
• Total variance = sum( square (prediction – A) )
Regression
•
Note that sum here is the
sum over all errors over all
predictions (where a single
error is calculated form a
single predictions)
Regression
•
What is the best value of R2
score you can hope for?
Regression
•
http://aiclub.world

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Pa2 session 7