Related Questions

Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps? Note: You have more than 1000 models predictions.
1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set.
2. Start with empty ensemble
3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set
What is the primary goal of time series modeling in data modeling?
Which of the following statements are true?
Statement I Big data refers to no precise quantity, but generally refers to data in the range of Petabytes and Exabyte, i.e. billions to trillions of records, all from various sources. Big data is generated much faster. than traditional data in much larger quantities.
Statement II Big data interests industries because they are able to learn more patterns and curious variances than smaller data sets, with the capability to provide valuable insights into consumer behaviour, weather systems, financial market activity, or other phenomena.
Statement III Various departments and product lines use various applications within most organisations and store data in separate datasets. These separate databases may contain similar data, but data from one data base to another is not always consistent. This is called data silo.
What is the primary goal of dimensionality reduction techniques in data modeling?
Certain number of children were playing a game of football. There was a trial taking place on the field. In this trial the probability that a goal is scored is 2/5. The probability that a girl hits a goal is 7/10 whereas the probability that a boy hits a goal is 1/5. What will be the probability that not only a goal is hit but it is also hit by a boy?
Certain numbers of children were playing a game of football. There was a trial taking place on the field. In this trial the probability that a goal is scored is 2/5. The probability that a gril hits a goal is 7/10 whereas the probability than a boy hits a goal is 1/5. What will be the probability that not only a goal is hit but it is also hit by a boy?
In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don't communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.
Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
Suppose, you have 2000 different models with their predictions and want to ensemble predictions of best x models. Now, which of the following can be a possible method to select the best x models for an ensemble?
If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?