Numpy

Question 1

Define an array at numpy

Answer 1

import numpy as np

a=np.array([0,1,2,4])

Question 2

Create a multidimensional array in numpy

Answer 2

b = np.array([[0, 1, 2], [3, 4, 5]]) # 2 x 3 array

Question 3

How to create a numpy array in one move?

Answer 3

np.arange(10)

Question 4

How to create an array with defined number of points between a range of numbers?

Answer 4

c = np.linspace(0, 1, 6)

Question 5

Create

a) An array of ones
b) An array of zeros
c) A diagonal array

Answer 5

a = np.ones((3, 3))  # reminder: (3, 3) is a tuple
b = np.zeros((2, 2)
c = np.eye(3

Question 6

Which is the most common data type in numpy?

Answer 6

Float data type

Question 7

Does slicing creates a new array?

Answer 7

No, it does not. It only creates a view

Question 8

Explain the array slicing with indexing

Answer 8

Indexing can be done with an array of integers,

a[[2, 3, 2, 4, 2]] # note: [2, 3, 2, 4, 2] is a Python list

Question 9

How to create a sparse matrix in CSR format?

Answer 9

sparse_matrix = sparse.csr_matrix(array)

Question 10

Name the difference between pandas and numpy

Answer 10

In contrast to NumPy, which requires that all entries in an
array be of the same type, pandas allows each column to have a separate type (for example, integers, dates, floating-point numbers, and strings)

Question 11

Explain what is samples,features and shape

Answer 11

We see that the array contains measurements for 150 different flowers. Remember
that the individual items are called samples in machine learning, and their properties
are called features. The shape of the data array is the number of samples multiplied by
the number of features

Question 12

What is a scatter?

Answer 12

A scatter plot of the data puts one feature along the x-axis and another
along the y-axis, and draws a dot for each data point

Question 13

Theoretical question:how nearest number algorithm works?

Answer 13

It assigns a new label to a new point given depending to its nearest before-known neighbour.

Question 14

How machine learning models are implemented in scikit-learn?

Answer 14

They are implemented at their own classes which are celled estimators.

Question 15

Import in scikit-learn the k nearest neighbour?

Answer 15

from scilearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=1)

Question 16

For k nearest neighbours in scilearn , how you build the model on the training set?

Answer 16

knn.fit(X_trained,Y_trained)

Question 17

Name the major categories of supervised learning

Answer 17

Classification and regression
In classification, the goal is to predict a class label, which is a choice from a predefined
list of possibilities.
For regression tasks, the goal is to predict a continuous number, or a floating-point
number in programming terms (or real number in mathematical terms).

Question 18

What does it actually mean for the model to generalize from the training set to the test set?

Answer 18

If a model is able to make accurate predictions on unseen data

Question 19

What is bunch objects in skilearn?

Answer 19

Bunch objects, which contain some information about the dataset
as well as the actual data. All you need to know about Bunch objects
is that they behave like dictionaries, with the added benefit that you
can access values using a dot (as in bunch.key instead of
bunch[‘key’]).

Question 20

How to count the occurrences for each element of a numpy array?

Answer 20

Use the following np.bincount(np.array)

Question 21

In a dataset, what is the interactions?

Answer 21

It is all the relationships between the features of a dataset.