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INET NGN HESI RN EXIT EXAM 2019 ALL 160 QUESTIONS & ANSWERS INCLUDED - GUARANTEED PASS A+
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INET NGN HESI RN EXIT EXAM 2019 ALL 160 QUESTIONS & ANSWERS INCLUDED - GUARANTEED PASS A+
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INET NGN HESI RN EXIT EXAM 2019 ALL 160QUESTIONS & ANSWERS INCLUDED - GUARANTEED
PASS A+
Statistical Reasoning - ANSWER: the way people reason with statistical ideas and
make sense of statistical information
curse of dimensionality - ANSWER: The curse of dimensionality refers to how certain
learning algorithms may perform poorly in high-dimensional data.
First, it's very easy to overfit the the training data, since we can have a lot of
assumptions that describe the target label (in case of supervised learning). In other
words we can easily express the target using the dimensions that we have.
Second,we may need to increase the number of training data exponentially, to
overcome the curse of dimensionality and that may not be feasible.
Third, in ML learning algorithms that depends on the distance, like k-means for
clustering or k nearest neighbors, everything can become far from each others and
it's difficult to interpret the distance between the data points.
Frequentist vs. Bayesian - ANSWER: Frequentist: Can estimate a mean from data, but
can never understand the 'actual' mean.
Bayesian: Says "only data is real". Mean is an abstraction, and some values are more
believable than others.
Central Limit Theorem - ANSWER: The theory that, as sample size increases, the
distribution of sample means of size n, randomly selected, approaches a normal
distribution.
Law of Large Numbers - ANSWER: the larger the number of individuals that are
randomly drawn from a population, the more representative the resulting group will
be of the entire population
Probability Distribution - ANSWER: a description of how the probabilities are
distributed over the values of the random variable
Assessing Skewed data - ANSWER: Positive = skewed right.
Negative = skewed left
Categorical Variable - ANSWER: a variable that names categories (whether with
words or numerals)
continuos variable - ANSWER: infinite number of values within a given range
, hypothesis testing - ANSWER: a decision-making process for evaluating claims about
a population
p-value - ANSWER: The probability of observing a test statistic as extreme as, or
more extreme than, the statistic obtained from a sample, under the assumption that
the null hypothesis is true.
t-test - ANSWER: a statistical test used to evaluate the size and significance of the
difference between two means
ANOVA - ANSWER: Analysis of variance. A statistical procedure examining variations
between two or more sets of interval or ratio data.
NULL - All population means are exactly equal.
Chi-square - ANSWER: Involves categorical variables. Looks at 2 distributions of
categorical data to see if they differ from each other.
NULL - Two categorical Variables are independent in some population.
Parametric vs. Non-parametric - ANSWER: Parametric: The number of parameters is
fixed (wrt to the sample size).
Non-Parametric: The number of parameters can grow with the sample size.
Supervised Learning - ANSWER: A type of model creation, derived from the field of
machine learning, in which the target variable is defined. Effectively, the model
attempts to find a function that maps the input variables to the output variable.
Unsupervised Learning - ANSWER: A type of model creation, derived from the field of
machine learning, that does not have a defined target variable. Often clustering.
Loss Function - ANSWER: The amount of error a model has. Goal - minimize the loss
function.
regularization - ANSWER: Regularization is a technique used in an attempt to solve
the overfitting problem in statistical models.
Term that discourages more complex models.
Underfitting - ANSWER: when a model is too simple, both training and test errors are
large
Overfitting - ANSWER: The process of fitting a model too closely to the training data
for the model to be effective on other data.
Bias - ANSWER: Bias: simplifying assumptions made by a model to make target
function easier to learn.
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