DATA SCIENCE QUESTIONS AND ANSWERS
Paired t-Test has its purpose in the testing is to determine whether there is statistical evidence that the mean difference between paired observations on a particular outcome is significantly different from zero. The Paired-Samples t Test is a parametric test. This test is also known as Dependent t-Test. Was the above useful? Please share with others on social media. If … Read more
Overfitting in mathematics and statistics is one of the most common tasks consisting in attempts to fit a “model” to a set of training data, so as to be able to make reliable predictions on generally untrained data. In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively … Read more
Out-Of-Sample Evaluation means to withhold some of the sample data from the model identification and estimation process, then use the model to make predictions for the hold-out data in order to see how accurate they are and to determine whether the statistics of their errors are similar to those that the model made within the sample of data that was … Read more
Outlier is an observation point that is distant from other observations. An outlier may be due to variability in the measurement or it may indicate an experimental error, the latter are sometimes excluded from the data set. Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution. … Read more
Nearest Neighbor Algorithm was one of the first algorithms used to determine a solution to the traveling salesman problem. In it, the salesman starts in a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one. The nearest neighbor algorithm is easy to implement and … Read more
Multiple Regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). The independent variables can be continuous or categorical (dummy … Read more
Multinomial Logistic Regression is the linear regression analysis to conduct when the dependent variable is nominal with more than two levels. Thus it is an extension of logistic regression, which analyzes dichotomous (binary) dependents. Since the output of the analysis is somewhat different to the logistic regression’s output, multinomial regression is sometimes used instead. Like all linear regressions, the multinomial … Read more
Model Fitting is running an algorithm to learn the relationship between predictors and outcome so that you can predict the future values of the outcome. It proceeds in three steps: First, you need a function that takes in a set of parameters and returns a predicted data set. Second you need an ‘error function’ that provides a number representing the … Read more
Markov Model in probability theory is a stochastic model used to model randomly changing systems where it is assumed that future states depend only on the current state not on the events that occurred before it (defined as the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in … Read more
Manhattan Distance is the distance between two points measured along axes at right angles. The name hints to the grid layout of the streets of Manhattan, which causes the shortest path a car could take between two points in the city. The limitation of the Manhattan Distance heuristic is that it considers each tile independently, while in fact, tiles interfere … Read more