{"id":198,"date":"2018-01-08T14:10:00","date_gmt":"2018-01-08T14:10:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/2018\/01\/08\/intelligent-data-analysis-quetions-and-answers-summary2017\/"},"modified":"2018-01-08T14:10:00","modified_gmt":"2018-01-08T14:10:00","slug":"intelligent-data-analysis-quetions-and-answers-summary2017","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/intelligent-data-analysis-quetions-and-answers-summary2017\/","title":{"rendered":"Intelligent Data Analysis Quetions and Answers Summary(2017)"},"content":{"rendered":"
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1. PARADIGMS OF LEARNING<\/b><\/span><\/p>\n
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Interpretation of Probability<\/b>
Probability expresses uncertainty that an even may or may not occur and is a key concept in pattern recognition.
Lets assume two random variables X<\/b> and Y<\/b> such that X<\/b> can take values xi<\/sub> where i = 1,…, M<\/i><\/span> and Y<\/b> can take values yi<\/sub><\/span><\/i> where i = 1,…,N.<\/span><\/i>
Also let the total number of times X takes the value xi<\/sub> by ci<\/sub> and the total number of times Y takes the value yi<\/sub><\/span><\/i> be cj<\/sub>.<\/i><\/span><\/p>\n

Note the Following:<\/i><\/span>
1. <\/i>The probability that X would take value xi and Y would take a value yi is written as:
p(X = xi<\/sub>, Y = yj<\/sub>) = nij<\/sub>\/N<\/i><\/span>
This is called the joint probability of X = xi<\/sub><\/i><\/span> and Y= yj<\/sub><\/span><\/i><\/p>\n

2. The probability that X would take value xi is given as
P(X = xi<\/sub>) = ci<\/sub>\/N<\/span><\/i><\/p>\n

Rules of Probability <\/span>
The two rule of probability are the sum and the product rule given below:<\/div>\n

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Bayesian Model<\/b>
Bayesian model of comparison involves the use of probabilities to represent uncertainty in the choice of model along with consistent application of the sum and product rules of probability.<\/p>\n

2. STATISTICAL DECISION THEORY<\/b><\/span><\/p>\n

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Optimal Decision<\/b>
Optima decision is in decision theory is a decision among the possible alternatives that is closest to the expected result<\/p>\n

Receiver Operating Characteristic Curve(ROC)<\/b>
This is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied<\/p>\n

Area Under the ROC Curve<\/b>
The AUC is equal to the probability that a classifier would rank a randomly chosen positive instance higher than a randomly chosen negative one.<\/p>\n

3. PHASES OF DATA ANALYSIS USING MACHINE LEARNING, VEDA<\/b><\/span><\/p>\n

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VEDA – Visual Exploratory Data Analysis
Exploratory data analysis in statistics and machine learning is a approach to analyzing data sets to summarize the key features of data, most time using visual methods.<\/p>\n

Study Design<\/b>
Study design is the aspect of Exploratory Data Analysis. A set of methods and procedures used in collecting and analyzing measures of variables specified in the research problem.  Design of a study specifies new study type an sub type.<\/p>\n

Exploratory vs Confirmatory Data Analysis<\/b>
While confirmatory data analysis tests a-priori hypothesis, that is the outcome prediction made before the measurement phase begins,
Exploratory seeks to generate a-posteriori pattern or other items in the dataset and looks for potential relations between the variables.<\/p>\n

What is Anomaly Detection?<\/b>
Anomaly detection is the identification of items, events or observations that do not conform to an expected pattern or other items in the dataset. Anomalies are referred to as outliers, novelties, noise, deviations or exceptions. Anomaly detection is carried out using any of the following methods:<\/p>\n