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[BEST & NEW] Model Validation Interview Questions and Answers

Last updated on 19th Dec 2022, Blog, Interview Question

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1. What is a model validation?

Ans:

Model validation is a process of verifying that a model meets all requirements that have been set for it. This includes verifying that model is accurate and complete, as well as verifying that it is consistent with the other models that have been developed.

2. Why do need to validate a models?

Ans:

There are a some reasons why model validation is be important. First, it helps ensured that the data are working with is clean and accurate. Second, it can help us to catch errors early on in development process, before they cause a major problems down the line. Finally, it helps to build more robust and reliable models overall.

3. Can explain what cross-validation is and how it works?

Ans:

Cross-validation is the technique used to assess the accuracy of model. It works by splitting a data into a training set and a test set. The model is fit on training set, and then predictions are made on a test set. The accuracy of model is then assessed by comparing predictions to the actual values in a test set.

4. How do perform a simple train/test split for a data using Python?

Ans:

Can use a train_test_split function from sklearn.model_selection module. This function will take in data as NumPy array or a pandas DataFrame, and it will return a two new arrays or DataFrames: one for training data and one for a testing data.

5. How can compute the accuracy of training set using Scikit-Learn?

Ans:

Can use accuracy_score function from a sklearn.metrics module to compute accuracy of training set.

6. How do use a grid search for the hyperparameter optimization in Python?

Ans:

Grid search is the method for hyperparameter optimization that involves a systematically testing different combinations of the hyperparameter values in order to find a combination that results in best performance for the model. In Python, and can use the GridSearchCV module from the scikit-learn library to perform a grid search.

7. Is it possible to check if model has overfit or underfit the training data? If yes, then how?

Ans:

Yes, it is possible to check if model has a overfit or underfit the training data. One way to do this is to look at training and validation accuracy. If the training accuracy is more higher than the validation accuracy, then it is likely that model has a overfit the training data. If the training accuracy is more lower than the validation accuracy, then it is likely that model has underfit a training data.

8. Can give some examples of real-world problems that require a model validation techniques?

Ans:

  • Ensuring that a machine learning model is more accurately predicting labels.
  • Checking that financial model is be correctly calculating risk.
  • Verifying that are physical model accurately predicts a behavior of a system.

9. What’s the difference between the k-fold cross-validation and iterated k-fold validation?

Ans:

The main difference between the k-fold cross-validation and iterated k-fold validation is that k-fold cross-validation will run through entire dataset k times, while an iterated k-fold validation will only run through a dataset once. This means that are k-fold cross-validation is more computationally expensive, but it also means that it will be more accurate in terms of a model performance.

10. When should avoid using a cross-validation?

Ans:

There are a few situations when might need to avoid using cross-validation. One is if are working with a very small dataset, as might not have enough data to split up and still have enough left over to train a model. Another is if working with the time series data, as cross-validation can lead to data leakage if are not careful. Finally, if working with the data that is not independent and identically distributed ,then cross-validation might not be best option.

11. What are some common metrics used to evaluate a machine learning models?

Ans:

There are few common metrics used to evaluate a machine learning models. One is accuracy, which measures how often model predicts the correct label for the given data point. Another is a precision, which measures how often a model predicts a positive label when a true label is positive. Finally, there is be recall, which measures how often model predicts a positive label when a true label is actually positive.

12. What is best way to find whether the parameters of a model have converged?

Ans:

There are a some different ways to determine whether parameters of the model have converged, but most common method is to simply check the value of objective function at each iteration. If objective function is not changing much from the one iteration to the next, then it is likely that the model has to be converged. Another common method is to check values of the gradient vector at each iteration. If gradient vector is close to zero, then this also indicates that a model has converged.

13. What do understand by a entropy and information gain?

Ans:

A random variable’s degree of uncertainty is measured by entropy. The higher entropy, the more uncertain the variable is. Information gain is a measure of how much entropy is reduced by knowing a value of a certain variable. In other words, it measures how much information is gained by knowing a value of a certain variable.

14. How would go about determining a feature importance?

Ans:

There are a few ways to go about determining a feature importance. One way would be to used a technique like decision trees, where the features that are used most often to made decisions are considered a most important. Another way would be to used a technique like linear regression, where a features that have the strongest correlation with target variable are considered most important.

15. What do know about a AUC-ROC curves?

Ans:

AUC-ROC curves are a graphical representation of performance of a binary classification model. The AUC-ROC curve is created by a plotting the true positive rate against a false positive rate. The AUC-ROC curve can be used to compared the performance of various models and to select a best model for a specific problem.

16. What is a precision and recall? How are different from each other?

Ans:

Precision and recall are the two metrics used to evaluate the performance of a machine learning model. Precision measures a percentage of correct positive predictions made by model, while recall measures the percentage of the positive cases that model correctly predicts.

17. How does an ROC curve help us to visualize the performance of a classifier?

Ans:

The ROC curve is graphical representation of how well a classifier can distinguish between the two classes. The curve is created by plotting the true positive rate against a false positive rate. The true positive rate is proportion of positive examples that are correctly classified, while the false positive rate is a proportion of negative examples that are incorrectly classified. A classifier that performs well will have high true positive rate and a low false positive rate, which will result in the ROC curve that is close to a top-left corner of the graph.

18. What is a Brier score? When is it useful?

Ans:

The Brier score is the measure of the accuracy of a probabilistic predictions. It is often used in a meteorology to score the accuracy of weather forecasts. The Brier score can be used in any situation where predictions are being made about likelihood of something happening.

19. Can explain what a confusion matrix is?

Ans:

A confusion matrix is a table that is used to evaluate an accuracy of a classification model. The table is made up of 4 different quadrants that represent a different possible outcomes of a classification. The first quadrant represents a true positives, the second quadrant represents false positives, a third quadrant represents false negatives, and the fourth quadrant represents a true negatives.

20. What is purpose of a sensitivity and specificity?

Ans:

Sensitivity and specificity are two statistical measures that are used to evaluate a performance of a diagnostic test or predictive model. Sensitivity measures a proportion of true positives that are correctly identified by a test or model, while specificity measures the proportion of true negatives that are correctly be identified.

21.What is Model Validation Technique?

Ans:

A model validation technique is the process used to ensure that a model is accurate and reliable. This can be done through the variety of methods, including testing a model against data from known sources, using a model to make predictions and then comparing those predictions to a actual outcomes, and analyzing a model’s structure and assumptions.

22.What are the different Model Validation techniques?

Ans:

  • Cross-validation.
  • Bootstrapping.
  • Simulation.
  • Statistical testing.

23.What is Cross-Validation technique?

Ans:

Cross-validation is technique for assessing how results of a statistical analysis will generalize to an independent data set. It is mainly used in a settings where the goal is be prediction, and one wants to estimate how accurately a predictive model will perform in a practice.

24.What is a Bootstrapping technique in Model Validation?

Ans:

Bootstrapping method is resampling technique used to estimate a distribution of a statistic by sampling with the replacement from the original dataset. This technique can be used with the any statistic, but is most commonly used when estimating a distribution of a statistic that is not normally distributed.

25.What is a Simulation technique in Model Validation?

Ans:

Simulation is a process of verifying the accuracy of a model by comparing a results of the model to real-world data. This technique can be used to verify accuracy of any type of model, including the statistical models, machine learning models, and physical models.

26.What is a Statistical testing technique in Model Validation?

Ans:

Statistical testing is used to validate a models by assessing the goodness of fit of aq model to the data. This technique can be used to assess both the linear and nonlinear models.

27.What are advantages of Model Validation techniques and Why do use Model Validation techniques?

Ans:

  • They can help to ensure that models are accurate and reliable.
  • They can helpto identify potential problems with a models before they are deployed.
  • They can help to improve the performance of the models.
  • They can help to understand the behavior of models better.

28.How do validate a model?

Ans:

Models can be validated by comparing a output to independent field or experimental data sets that align with a simulated scenario.

29.What is a model validation in credit risk?

Ans:

Model validation involves the processes and activities that are verify models are performing as a intended, and is a core element of model risk management (MRM).

30.What are 5 types of validation?

Ans:

  • Data Type Check. A data type check confirms that a data entered has correct data type.
  • Code Check. A code check ensures that a field is selected from the valid list of values or follows as a certain formatting rules.
  • Range Check.
  • Format Check.
  • Consistency Check.
  • Uniqueness Check.

31.What are 3 validation rules?

Ans:

A Validation rule and validation text examples:

  • Value must be a zero or greater. must enter the positive number.
  • Value must be an either 0 or greater than a 100.

32.What are four types of validation?

Ans:

  • Prospective validation (or premarket validation).
  • Retrospective validation.
  • Concurrent validation.
  • Revalidation.

33.What are 4 steps of modeling?

Ans:

Attention: Observing a model’s behavior.

Retention: Remembering what are observed.

Reproduction: Imitating a model’s behavior.

Motivation: Having good reason to reproduce a behavior.

34.What are five steps in validation process?

Ans:

(1) preparing to a conduct validation, (2) conduct a planned validation (perform validation), (3) analyze a validation results, (4) prepare a validation report, and (5) capture a validation work products.

35.What are the 6 levels of validation?

Ans:

Level One: Stay Awake and also Pay Attention.

Level Two: to Accurate Reflection.

Level Three: Stating What Hasn’t Been Said Out a Loud.

Level Four: Validating by Using Past History or Biology.

Level Five: Normalizing.

Level Six: Radical Genuineness.

36.What are the three critical aspects of validation?

Ans:

Validation determines if assessment tools have a produced an intended evidence. Validators must look at an evidence in the sample, and determine if it is a valid, reliable, sufficient, current and authentic.

37.What are the two validation examples?

Ans:

For example, when signing up for the user account on a website, validation might include: presence check – a username must be entered. length check – password must be at least eight characters a long.

38.What is a first step of validation?

Ans:

Write Down Goals, Assumptions, and Hypotheses Writing down a goals of a business is the first step in market validation. The process of articulating a vision can illuminate any assumptions that have and provide an end goal.

39.What are the validation techniques?

Ans:

Product Validation Techniques are various tests and experiments that aim to make sure that are feature (that often occurs as solution in an Opportunity Solution Tree and is described in the Product Requirements Document or PRD) will succeed, before investing a developer time to build it.

40.What is a validation life cycle?

Ans:

The Validation Life Cycle is the implementation mechanism which can assist a pharmaceutical manufacturers in an organization and execution of validation activities. A considerable body of work exists which identifies how to validate a processes of different type and description.

41.What are rules of validation?

Ans:

A validation rule can contain a formula or expression that evaluates a data in one or more fields and returns the value of “True” or “False”. Validation rules also included an error message to display to user when the rule returns the value of “True” due to an invalid value.

42.What are standards of validation?

Ans:

Validation is means of ensuring that are requirements in a standard specify what they are supposed to specify.

43.What are challenges of validation?

Ans:

  • Applying the 80/20 Rule to Common Validation Issues.
  • Missing Information.
  • Inconsistency.
  • Lack of Needed Detail.
  • Traceability.
  • Vague wording.
  • Unverifiable test results.
  • GDP.

44.What is purpose of a validation?

Ans:

Validation is intended to ensured a product, service, or results in the product, service, or system that meets an operational needs of the user.

45.What are common mistakes to avoid validation?

Ans:

  • Believing assumptions to be a truth.
  • Not understanding a customers’ perspective.
  • Validating is a product instead of an idea.
  • Not testing a competitor’s products.

46.What are validation tools?

Ans:

  • Datameer.
  • Talend.
  • Informatica.
  • QuerySurge.
  • ICEDQ.
  • Datagaps ETL Validator.
  • DbFit.
  • Data-Centric Testing.

47.What is other name of validation?

Ans:

Some common synonyms of a validate are authenticate, confirm, corroborate, substantiate, and verify. While all these words mean “to attest to a truth or validity of something,” validate are implies establishing validity by an authoritative affirmation or by factual proof.

48.What is a validation in QMS?

Ans:

QMS Validation is the process of checking that a system or process is compliant with a explained set of requirements. It’s an essential part of a quality control, which refers to a various actions taken to improve a quality of goods and services.

49.Why is a validation important in modeling?

Ans:

Validating a machine learning model outputs are important to ensure its accuracy. When machine learning model is trained, a huge amount of training data is used and the major aim of checking a model validation provides an opportunity for machine learning engineers to improved the data quality and quantity.

50.How do pass a validation rule?

Ans:

Update a validation rule are want the process or flow to bypass by adding a check for an Is Automation Bypassed field is set to a false. If Is AutomationBypassed = true (which process or flow will update for a formula checkbox to evaluate to true), then validation rule will be bypassed.

51.How do stop a fighting for validation?

Ans:

  • Replace That Mean Voice In a Head.
  • Surround Yourself With a Nice People.
  • Check an Accuracy Of Beliefs.
  • Remember To a Practice.
  • Try To Understand Why are Seeking Approval.
  • Make A To-Do List.
  • Write Down a Five Daily Accomplishments.
  • Keep a Goals Realistic.

52.What causes are validation error?

Ans:

Validation errors typically occur when a request is to be malformed — usually because field has not been given a correct value type, or the JSON is misformatted.

53.What is the unhealthy validation?

Ans:

What does an unhealthy reliance on an external validation look like? Not being able to confront people or disagree, changing a thoughts and beliefs because someone else either approves or disapproves, and ascribing a self-worth to the approval of others — all are examples of a reliance on an external validation.

54.What is a poor validation?

Ans:

Fortify “Cross-Site Scripting: Poor Validation” is a complaining that are OUTPUT encoding is either improper or not effective. The purpose of a output encoding (escaping) is to confine a special characters (meta char) as literal string, so they cannot be executed as command.

55.Which language is used for the validation?

Ans:

Scripting languages like JavaScript and VBScript are used for a client-side validation.

56.How to do model validation?

Ans:

  • Create a Development, Validation and Testing Data Sets.
  • Make a Model with a Training Data Set.
  • Compute a Statistical Values Identifying a Model Development Performance.
  • Calculate a Model Results to a Data Points in the Validation Data Set.

57.What is a model validation in ML?

Ans:

Model validation is the phase of machine learning that quantifies a ability of an ML or statistical model to produce a predictions or outputs with enough fidelity to be used for reliably to achieve a business objectives.

58.How do validate a ML model?

Ans:

  • Train/test split.
  • k-Fold Cross-Validation.
  • Leave-one-out Cross-Validation.
  • Leave-one-group-out Cross-Validation.
  • Nested Cross-Validation.
  • Time-series Cross-Validation.
  • Wilcoxon signed-rank test.
  • McNemar’s test.

59.Which of following are elements of a model validation?

Ans:

  • Conceptual Design.
  • System Validation.
  • Data Validation and Quality Assessment.
  • Process Validation.

60.What is a role of model validation in a machine learning?

Ans:

In a machine learning, model validation is referred to as a process where a trained model is evaluated with the testing data set. The testing data set is the separate portion of same data set from which are training set is derived.

61.Why are 3 batches required for a validation?

Ans:

When two batches are taken as a validation the data will not be sufficient for an evaluation and to proved a reproducibility because statistical evaluation cannot be done on a two points, it needs a minimum three points because two points always draw a straight line.

62.What is a validation in QA?

Ans:

Establishing written evidence that a certain procedure will consistently result in a product fulfilling its planned requirements and quality features is known as “validation” (6).

63.Which testing is used for the validation?

Ans:

Important validation testing techniques are include a unit testing, integration testing and system testing. These are all various types of functionality testing, which can find if various elements of a software function according to user requirements.

64.What is a validation in SDLC?

Ans:

Validation is a process of evaluating software at the end of development process to find whether software meets a customer expectations and requirements. The objective of a Verification is to make sure that a product being develop is as per requirements and design specifications.

65.Is a validation a part of QA?

Ans:

The objective of this work is to an overview the process validation in a different pharmaceutical processes. Quality is a most important requirement in manufacturing process. All drugs must be manufactured to a highest quality level.

66.Who is a responsible for validation?

Ans:

Validation of a system is an ultimate responsibility of the user. Here, the user is a healthcare or pharmaceutical company that is using the product or process with the software to report to a regulatory bodies.

67.Does a validation mean testing?

Ans:

Validation is a process of checking whether a software product is up to the mark or in other words product has a high level requirements. It is a process of checking validation of product .

68.What are major reasons for a validation?

Ans:

One of key reasons for a validation is to understand a sources of variability in the manufacturing process and once known to control the variability in a order to consistently produce products that meet a specification.

69.What is a benefit of validation?

Ans:

It ensures a quality of manufactured products and helps to manufacture a quality products. Following are the benefits of validation of any system or process: 1. Process parameters and controls are found during the validation of any process or system.

70.What is a scope of validation?

Ans:

Validate Scope is a process of formalizing acceptance of completed project deliverables. A process that shows a stakeholders have received what was agreed upon and formalizes their approval. It is a primarily concerned with a recognition of the product by validating each deliverable.

71.What is model validation in AML?

Ans:

An AML model validation is the process with the intent to test that a model is performing as expected and that a style is in line with the financial institutions (FIs) objects and business uses.

72.What is difference between the model validation and calibration?

Ans:

Validation is the process of comparing the model and its behavior to a real system and its behavior. Calibration is an iterative process of comparing a model with real system, revising a model if necessary, comparing again, until a model is accepted .

73.What is difference between the model validation and verification?

Ans:

The distinction between two terms is largely due to role of specifications. Validation is a process of checking whether a specification captures a customer’s requirements, while verification is a process of checking that are software meets specifications.

74. What is a benchmarking in model validation?

Ans:

Benchmarking is when a validator is providing a comparison of model being validated to the some other model or metric. The type of benchmark utilized will vary, like all the model validation performance testing does, with a nature, use, and type of model being validated.

75.What is most common validation requirement?

Ans:

Prototyping: In this validation techniques a prototype of the system is presented a before the end-user or customer, they experiment with the presented model and check if it meets their need. This type of model is generally used to collect a feedback about requirement of the user.

76.What happens in model validation?

Ans:

Model validation refers to a process of confirming that a model actually achieves its intended purpose. In a most situations, this will involve a confirmation that the model is predictive under a conditions of its intended use.

77.What is a KS in model validation?

Ans:

Kolmogorov-Smirnov (KS) test a measures the separation between the cumulative % event and cumulative % non-event. It is observed that a KS test statistics are less than 40, indicating that are model is not able to separate an events and non-events.

78.What is a risk model validation?

Ans:

Model validation involved the processes and activities that verify a models are performing as intended, and is a core element of model risk management (MRM).

79.What is an ECL validation?

Ans:

ECL Model Validation denotes a processes used to ensure that a credit risk assessment and measurement models used for a derivation of Expected Credit Loss are able to generate a accurate, consistent and unbiased predictive estimates, on ongoing basis.

80.What happens when a validation fails?

Ans:

When a processing invalid or corrupt data, an application might return a incorrect results, fail to load, or even crash a web server. Missing or insufficient input validation can also degrade a user experience on the other levels.

81.What is a model validation in banking?

Ans:

Model validation is a set of processes and activities intended to verify that are models are performing as expected, in line with their design objectives and business uses. An Effective validation helps to ensure that models are sound.

82.What is model validation in MVC?

Ans:

Model validation is a process of checking whether a user input is suitable for a model binding and if not it should provide a useful error messages to the user.

83.What does model validation analyst do?

Ans:

The Model/Anlys/Valid Analyst II is developing a professional role. Applies a specialty area knowledge in a monitoring, assessing, analyzing and/or evaluating processes and data. Identifies a policy gaps and also formulates policies.

84.What are elements of validation?

Ans:

  • Introduction and Scope.
  • System Overview.
  • Organizational Structure.
  • Quality Risk Management.
  • Validation Strategy.
  • Deliverables.
  • Acceptance Criteria.
  • Change Control.

85.What is a modeling in Excel?

Ans:

Excel modeling is a process where an individual uses spreadsheet to make a quantitative predictions based on the series of underlying assumptions.

86.What are three methods of a modelling?

Ans:

These methods are regression analysis, network analysis, and also computer simulation. Case studies are provided as a examples of these approaches.

87.What are 4 components of the model are?

Ans:

Effective modeling involves a 4 components to mix/match depending on a students and their experience: a clear GOAL, a positive DEMONSTRATION, a chance to be PRACTICE, and the opportunity to be REFLECT.

88.What is concept of a validation?

Ans:

Validation is the process of establishing documentary an evidence demonstrating that a procedure, process, or activity carried out in a production or testing maintains a desired level of compliance at all stages.

89.How is a validation done?

Ans:

Validation is a done during testing like feature testing, integration testing, system testing, load testing, compatibility testing, stress testing, etc. Validation are helps in building the right product as per a customer’s requirement and helps in satisfying their needs.

90.What are 6 Types of Validation Controls in ASP.NET?

Ans:

  • RequiredFieldValidator Control.
  • RangeValidator.
  • RegularExpressionValidator.
  • CompareValidator Control.
  • CustomValidator Control.
  • ValidationSummary Control.

91.Which algorithm is used for a credit risk analysis?

Ans:

The SVM model with a polynomial kernel is a best model of the four models because it has a highest accuracy and AUC value. Thus, this model can be used to classify a prospective customers into good credit or bad credit class with sufficiently more accuracy so as to help banks reduce a risk of bad credit.

92.Is a double entry a validation technique?

Ans:

Verification is making sure that an information on the source document is a same as the object document. Two main methods of a verification: double entry and also manual verification.

93.What are rules of double entry?

Ans:

The principles to be followed while recording a double-entry system of bookkeeping are as follows: Debit is written to a left, credit on the right. Every debit must have corresponding credit. Debit receives a benefit, and credit gives the benefit.

94.What comes a first verification or validation?

Ans:

Validation is a done at the end of the development process and takes place after verifications are be completed. Advantages of Verification: During verification if some defects are to be missed, then during validation process they can be caught as failures.

95.What is a contra entry?

Ans:

A contra entry is to be recorded when the debit and credit affect a same parent account and resulting in net zero effect to the account. These are transactions that are recorded between the cash and bank accounts.

96.Who is responsible for the validation?

Ans:

Validation of a system is the ultimate responsibility of the user. Here, the user is t a healthcare or pharmaceutical company that is using the product or process with the software to report to regulatory bodies.

97.What are major reasons for the validation?

Ans:

One of the key reasons for a validation is to understand a sources of variability in the manufacturing process and once known to control the variability in order to be consistently produce products that meet a specific ratio.

98.What is validation strategy?

Ans:

In a software testing terminology, validation strategy implies a cross referencing functionality of a software with requirement specification, to assess that it adheres to a prescribed demands of the client. Verification and validation are two terms that go parallel.

99.What is verification matrix?

Ans:

A Requirement Verification Matrix (RVM) is usually composed (1) requirement identification code, (2) requirement traceability to be higher level documents, (3) verification a methods to be used, (4) the stage(s) where verification takes place and (5) a verification procedure identification code.

100.What is goal of validation?

Ans:

Validation is an intended to ensure a product, service, or system results in product, service, or system ,that meets an operational needs of the user.

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