Exploratory data analysis

ByAdmin July 10, 2021

Week – 2 Exploratory data analysis >>> How to Win a Data Science Competition Learn from Top Kagglers

Z = X / Y

Z = X Y

Z = X - Y

Z = X + Y

2

(note that it is not the same variable $X$ as in previous questions).

Which hypotheses about variable $X$ do NOT contradict with the plots? In other words: what hypotheses we can’t reject (not in statistical sense) based on the plots and our intuition?

2 points

2\leq X<3

happens more frequently than

3\leq X<4

X

is a counter or label encoded categorical feature

X

can be the temperature (in Celsius) in different cities at different times

X

can take a value of zero

X

takes only discrete values

We use target variable $y$ to colorcode the points.

The other three plots were produced by jittering $X$ and $Y$ values

That is, we add Gaussian noise to the features before drawing scatter plot.

Select the correct statements.

2 points

Target is completely determined by coordinates

(x, y)

, i.e. the label of the point is completely determined by point’s position

(x, y)

. Saying the same in other words: if we only had two features

(x, y)

, we could build a classifier, that is accurate 100% of time.

It is always beneficial to jitter variables before building a scatter plot

Top right plot is “better” than top left one. That is, every piece of information we can find on the top left we can also find on the top right, but not vice versa.

Standard deviation for Jittering is the largest on the bottom right plot.

We need to jitter variables not only for a sake of visualization, but also because it is beneficial for a model.

How to Win a Data Science Competition

Graded quiz of How to Win a Data Science Competition
ByAdmin July 11, 2021

Week – 4 Graded quiz How to Win a Data Science Competition Learn from Top Kagglers 1. Which hyperparameters are first to tune in sklearn’s RandomForest? 1 point n_estimators, max_depth,…

Read More Graded quiz of How to Win a Data Science Competition
How to Win a Data Science Competition

Feature preprocessing and generation with respect to models
ByAdmin July 10, 2021

Week – 1 Feature preprocessing and generation with respect to models 1. Suppose we have a feature with all the values between 0 and 1 except few outliers larger than…

Read More Feature preprocessing and generation with respect to models
How to Win a Data Science Competition

All Assignment of How to Win a Data Science Competition
ByAdmin July 10, 2021

All Assignment of How to Win a Data Science Competition Learn from Top Kagglers Week – 1 Assignment Programming Assignment: Pandas basics Click Here To Get Week – 2 Assignment…

Read More All Assignment of How to Win a Data Science Competition
How to Win a Data Science Competition

Feature extraction from text and images
ByAdmin July 10, 2021

Week – 1 Feature extraction from text and images >>> How to Win a Data Science Competition: Learn from Top Kagglers 1. Select true statements about n-grams 2 points N-grams…

Read More Feature extraction from text and images
How to Win a Data Science Competition

Graded Soft/Hard Quiz of How to Win a Data Science Competition
ByAdmin July 10, 2021

Week – 1 Graded Soft/Hard Quiz of How to Win a Data Science Competition Learn from Top Kagglers 1. Which library provides the most convenient way to perform matrix multiplication?…

Read More Graded Soft/Hard Quiz of How to Win a Data Science Competition
How to Win a Data Science Competition

Validation
ByAdmin July 10, 2021

Week – 2 Validation >>> How to Win a Data Science Competition: Learn from Top Kagglers 1. Select true statements 1 point Performance increase on a fixed cross-validation split guaranties…

Read More Validation

Week – 2 Exploratory data analysis >>> How to Win a Data Science Competition Learn from Top Kagglers

Similar Posts