A Confusion Matrix

What is a Confusion Matrix.A confusion matrix is a table that is used to describe the performance of a classificationmodel (or “classifier”) on a set of test data for which the true values are known.What is Type 1 Error and Type 2 Error in a confusion matrix. Using an exampleexplain the each of the values in a confusion matrix.(5 marks).Type I Error (False Positive Error)-is asserting something as true when in reality it isfalse. Example, predicting that someone is pregnant she is not. The actual outcomebecame false when the prediction was positive.Type II Error ( False negative): This is when a result of testing shows that a conditionfailed while its actual was successfully. This error is committed when we fail to believe atrue condition. Consider the pregnancy example, When we predict that some one isn’tpregnant when in actual sense she is pregnant.Kenneth 54. What is an ROC curve.Is a graphical representation of the ability of binary classifier, in diagnosis, while itsdiscrimination threshold varied. Originally, ROC was used in the theory of signaldetection, however its application has extended to medicine, natural hazards, radiologyand machine learning.Give five advantages of using ROC curve in healthcare data.(5 marks) Several diagnostic tasks on a particular subjects could be compared at the sametime in a ROC space.By visualizing the curve, sensitivity at a specific FPF can be obtained easily. ROC curve analysis can be used to determine optimal cut-off value.In contrast to single measures of sensitivity and specificity, the diagnosticaccuracy, such as AUC driven from this analysis is not affected by decisioncriterion and it is also independent of prevalence of disease since it is based onsensitivity and specificity.. What is overfitting? What is Underfitting? What happens to an over fitted modelwhen it is deployed in the real world application.(5 marks)Overfitting is a situation where a model cannot generalize or fit well on datesets whichare unseen. A sign of overfitting is when the error on the testing dataset is greater than theerror on the training dataset.Kenneth 6Underfitting refers to a model that can neither model the training dataset nor generalizeto new dataset.. A Machine learning with under-fitting is not appropriate because it haspoor performance on the training datasets.6. What is an outliers?If a data point if considerably far from other data points then its an outlier. Take anexample of a classroom where every student is of an average height except threeextremely tall ones, the three data points, in this case, become outliers.What do you do when you have skewed data and outliers?Below are some of the approaches to undertake.Setup a filter in the testing tool to eliminate skewed and outliers.Change the values of outliers or skewed data to according to your data.Remove or change value of outliers during post testing analysis.Why is it important to normalize data before feeding to a supervised NN.Normalization of data ensures that, anomalies that may complicate the data analysis areeliminated. These anomalies may come as a result of deleting data, updating existinginformation, and creating more new information.7. What is deep learning?Kenneth 7According to machinelearningmastery.com, deep learning is a subfield of machinelearning concerned with algorithms inspired by the structure and function of the braincalled artificial neural networks.Why has deep learning gained popularity in the recent years?Deep learning is superior in giving accurate results on very huge datasets, when trainedGive one example where you would apply deep learning to solve the problem andwhy.Automated Driving: as an automotive researcher,i would use deep learning to detect stopsigns, traffic light and pedestrians on the road.I would use deep learning because it is superior in predicting outcome of hugedatasets.The system will possibly train itself to give accurate prediction as automation onroad is very crucial.8. What is convolutional neural network?Is a kind of algorithm of deep learning that recognizes images by taking different imageinputs, assign weights and bias in various aspects of those images.How is it different from deep learning and neural network?How CNN differ from NN- convolutional-neural-network is a subclass of neural-networks which have at least one convolution layer while CNN, has one or more layersof convolution units. A convolution unit receives its input from multiple units from theprevious layer which together create a proximityKenneth 8How CNN differ from deep learning- a convolutional neural network is a class ofdeep neural network,commonly applied to analyze visual imagery while deeplearning have several other classes.Type I Error (False Positive Error)-is asserting something as true when in reality it is false. Example, predicting that someone is pregnant she is not. The actual outcome became false when the prediction was positive.

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