🏁 Wrap-up quiz 1#

This quiz requires some programming to be answered.

Open the dataset ames_housing_no_missing.csv with the following command:

import pandas as pd
ames_housing = pd.read_csv("../datasets/ames_housing_no_missing.csv")

target_name = "SalePrice"
data, target = ames_housing.drop(columns=target_name), ames_housing[target_name]
target = (target > 200_000).astype(int)

ames_housing is a pandas dataframe. The column “SalePrice” contains the target variable.

We did not encounter any regression problem yet. Therefore, we convert the regression target into a classification target to predict whether or not an house is expensive. “Expensive” is defined as a sale price greater than $200,000.

Question

Use the data.info() and data.head() commands to examine the columns of the dataframe. The dataset contains:

  • a) only numerical features

  • b) only categorical features

  • c) both numerical and categorical features

Select a single answer

Question

How many features are available to predict whether or not a house is expensive?

  • a) 79

  • b) 80

  • c) 81

Select a single answer

Question

How many features are represented with numbers?

  • a) 0

  • b) 36

  • c) 42

  • d) 79

Select a single answer

Hint: you can use the method df.select_dtypes or the function sklearn.compose.make_column_selector as shown in a previous notebook.

Refer to the dataset description regarding the meaning of the dataset.

Question

Among the following columns, which columns express a quantitative numerical value (excluding ordinal categories)?

  • a) “LotFrontage”

  • b) “LotArea”

  • c) “OverallQual”

  • d) “OverallCond”

  • e) “YearBuilt”

Select all answers that apply

We consider the following numerical columns:

numerical_features = [
  "LotFrontage", "LotArea", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2",
  "BsmtUnfSF", "TotalBsmtSF", "1stFlrSF", "2ndFlrSF", "LowQualFinSF",
  "GrLivArea", "BedroomAbvGr", "KitchenAbvGr", "TotRmsAbvGrd", "Fireplaces",
  "GarageCars", "GarageArea", "WoodDeckSF", "OpenPorchSF", "EnclosedPorch",
  "3SsnPorch", "ScreenPorch", "PoolArea", "MiscVal",
]

Now create a predictive model that uses these numerical columns as input data. Your predictive model should be a pipeline composed of a sklearn.preprocessing.StandardScaler to scale these numerical data and a sklearn.linear_model.LogisticRegression.

Question

What is the accuracy score obtained by 10-fold cross-validation (you can set the parameter cv=10 when calling cross_validate) of this pipeline?

  • a) ~0.5

  • b) ~0.7

  • c) ~0.9

Select a single answer

Instead of solely using the numerical columns, let us build a pipeline that can process both the numerical and categorical features together as follows:

  • numerical features should be processed as previously done with a StandardScaler;

  • the left-out columns should be treated as categorical variables using a sklearn.preprocessing.OneHotEncoder. To avoid any issue with rare categories that could only be present during the prediction, you can pass the parameter handle_unknown="ignore" to the OneHotEncoder.

Question

One way to compare two models is by comparing the cross-validation test scores of both models fold-to-fold, i.e. counting the number of folds where one model has a better test score than the other. Let’s compare the model using all features with the model consisting of only numerical features. Select the range of folds where the former has a better test score than the latter:

  • a) [0, 3]: the pipeline using all features is substantially worse than the pipeline using only numerical feature

  • b) [4, 6]: both pipelines are almost equivalent

  • c) [7, 10]: the pipeline using all features is substantially better than the pipeline using only numerical feature

Select a single answer