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Sanat Kumar

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    Sanat Kumar
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    Northern Trust
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  1. Model cross validation is the method commonly used in Machine Learning which helps in estimating the variability (consistency) and reliability (performance of model over a period of time) of a model. While creating a model there are 2 things: 1) Train the data (which means estimating the parameters) 2) Test the algorithm (evaluate the performance) Out of the total data collected partial data is used for creating the model and partial for testing the model. Mostly multiple sets of data are from the population and testing is performed on these sets using dif
  2. Water Spider or Mizusumashi is instrumental for implementing Lean and removal of wastage Water spiders in lean referred to those individuals who are at a constant move on the job floor to ensure that all the workstations are working smoothly (like the water beetle or water spider, which continuously moves below the surface of the water). He ensures that’s all that the requirements of each work stations are fulfilled and there is continuous flow of production. At times his role could be confused from the Material Management or “go fetch” person but its much bigger than that, hi
  3. FMEA (Failure mode effect analysis) is a mechanism to identify step by step all possible failure modes present in a process/design/service/software so on. On the basis of three components “Severity, Occurrence and Detection” RPN (Risk Priority Number) is calculated which is product of S*O*D. Higher the RPN high is the risk involved in the step (general perception). Basis on this, there is concept of threshold RPN used by many organization. Some of the common way of identification of threshold RPN: Normally S,O and D are graded on a 10 scale metrics (where 1
  4. Class Imbalance refers when the class (es) is/are skewed or baised (or not in proportion). For eg.- If we want to compare the performance of boys against girls in a Higher Secondary School, but if the boys: girls ratio is not 1:1 then its imbalance (eg- 5:1, or 1:5 so on) Class imbalance could be minimal or maximum (higher the class imbalance less is the accuracy of prediction) Class Imbalance impacting predictive learning using simple example Eg- Let’s take example a school want to create a predictive model to understand student performance based on their mo
  5. In parametric statistics, when we have to compare means of two samples there are several test where 2 samples are dependent and independent 2 Sample T Test are used to compare the means of 2 sample which are not dependent on each other and have equal variance (determined by F test) which is normally distributed. Example: If we want to compare performance of two team (sales performance) Comparing the runs scored by two different team Outcome of a drug testing on 2 independent groups Wherein Paired Sample T test is performed to compare means of 2 samp
  6. In inferential statistics, Null and Alternate Hypothesis are defined (Ho- Null and Ha- Alternate) to conduct hypothesis testing. Null Hypothesis – states population parameters or commonly known facts Alternate Hypothesis – is the statement that one wants to prove using statistics (normally one defines alternate hypothesis first) Post defining hypothesis a sample is selected from population for the test and based on the p value either Null is accepted or Fail to accept Null Hypothesis P value < significance value (α) –Fail to accept Null hypothesis P va
  7. In Analyze phase of DMAIC, Xs’ (or causes) are identified basis different methodology of which Brainstorming is widely used. Post identification of X’s prioritization of X’s are done, where impact of X’s are studied over Y (effect). Prioritization could be performed by 2 ways “Data door or Process Door” Data door- For all the X’s where data is quantitative in nature (which can be measured). Data door method is taken. Commonly used tool for Data door approach are: Graphical Analysis: Trend Chart, Run Chats, Control Chats, Histogram etc Hypothesis Testing: Annova, Regr
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