Machine Learning

Question

What’s the best language to learn if I want to get involved with machine learning?

Answer 1

Machine learning algorithms are best implemented in Python, R, MATLAB and Julia. Of those, R and MATLAB are pretty hermetic, while Julia is relatively new, but growing strong.

I would (and did ;) focus on Python 🐍

Answer 2

PHP
VBScript
SMX – dedicated to web pages
Tcl – server-side in NaviServer and an essential component in electronics industry systems
WebDNA – dedicated to database-driven websites

AngelScript
Ch
EEL
Io
Julia
Lua
MiniD
Python
Ruby (via mruby)
Squirrel
Tcl

Answer 3

php is the best i think

Answer 4

Python is the current favourite.

Answer 5

Let’s say we have a feature matrix X. Drag and drop in order to correctly define X_train and X_test for the second fold.

Answer:
kf = KFold(n_splits=5, shuffle=True)
splits = list(kf.split(X))
a, b = splits[1]
X_train = X[a]
X_test = X[b]

Answer 6

Which of the following could be the output of this code assuming X has 3 datapoints?

kf = KFold(n_splits=3, shuffle=True)
splits = list(kf.split(X))
print(splits[0])

Answer:
([0, 2], [1])
([0, 1], [2])

Answer 7

Complete the code to do a k-fold cross validation where k=5 and calculate the accuracy. X is the feature matrix and y is the target array.

scores = [ ]
kf = KFold(n_splits=5, shuffle=True)
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = LogisticRegression()
model.fit(X_train, y_train)
scores.append(model.score(X_test, y_test))
print(np.mean(scores))

Answer 8

Complete the code to create a fourth feature matrix that has just the Pclass and Sex features and uses the score_model function to print the scores. Assume we’ve defined y to be the target values and kf to be the KFold object.

X4 = df[['Pclass', 'male']].values
score_model(X4, y, kf)

Answer 9

Select all that apply for the version of Decision Trees we are using.

Select all that apply

A feature can only be used once
Every leaf node has a prediction for the target value
Each internal node has exactly two children
Every feature must be used
Every path to a leaf node will be the same length

Answer:
Every leaf node has a prediction for the target value
Each internal node has exactly two children

Answer 10

scores = [ ]
kf = KFold(n_splits=5, shuffle=True)
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = LogisticRegression()
model.fit(X_train, y_train)
scores.append(model.score(X_test, y_test))
print(np.mean(scores))

Answer 11

scores = [ ]

kf = KFold(n_splits=5, shuffle=True)

for train_index, test_index in kf.split(X):

X_train, X_test = X[train_index], X[test_index]

y_train, y_test = y[train_index], y[test_index]

model = LogisticRegression()

model.
fit
(X_train, y_train)

scores.
append
(model.score(X_test, y_test))

print(np.
mean
(scores))

Answer 12

many thankssss

Answer 13

Let’s say we have a feature matrix X. Drag and drop in order to correctly define X_train and X_test for the second fold.

kf = KFold(n_splits=5, shuffle=True)
splits = list(kf.split(X))
a, b = splits[]
X_train = X[]
X_test = X[]
a b 1 0 2 ab

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