Classification¶

This tutorial uses safeds on titanic passenger data to predict who will survive and who will not, using sex as a feature for the prediction.

1. Load your data into a Table, the data is available under docs/tutorials/data/titanic.csv:

In [1]:

from safeds.data.tabular.containers import Table

titanic = Table.from_csv_file("data/titanic.csv")
#For visualisation purposes we only print out the first 15 rows.
titanic.slice_rows(0,15)

from safeds.data.tabular.containers import Table titanic = Table.from_csv_file("data/titanic.csv") #For visualisation purposes we only print out the first 15 rows. titanic.slice_rows(0,15)

Out[1]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	42.0000	0	0	C.A. 5547	3	7.5500	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	13.0000	0	2	C.A. 2673	3	20.2500	NaN	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	male	16.0000	1	1	C.A. 2673	3	20.2500	NaN	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0000	1	1	C.A. 2673	3	20.2500	NaN	Southampton	1
4	4	Abelseth, Miss. Karen Marie	female	16.0000	0	0	348125	3	7.6500	NaN	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	male	25.0000	0	0	348122	3	7.6500	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	male	30.0000	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0000	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	male	20.0000	0	0	SOTON/O2 3101284	3	7.9250	NaN	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18.0000	0	0	2657	3	7.2292	NaN	Cherbourg	1
10	10	Adahl, Mr. Mauritz Nils Martin	male	30.0000	0	0	C 7076	3	7.2500	NaN	Southampton	0
11	11	Adams, Mr. John	male	26.0000	0	0	341826	3	8.0500	NaN	Southampton	0
12	12	Ahlin, Mrs. Johan (Johanna Persdotter Larsson)	female	40.0000	1	0	7546	3	9.4750	NaN	Southampton	0
13	13	Aks, Master. Philip Frank	male	0.8333	0	1	392091	3	9.3500	NaN	Southampton	1
14	14	Aks, Mrs. Sam (Leah Rosen)	female	18.0000	0	1	392091	3	9.3500	NaN	Southampton	1

2. Split the titanic dataset into two tables. A training set, that we will use later to implement a training model to predict the survival of passengers, containing 60% of the data, and a testing set containing the rest of the data. Delete the column survived from the test set, to be able to predict it later:

In [2]:

split_tuple = titanic.split_rows(0.60)

train_table = split_tuple[0]
testing_table = split_tuple[1]

test_table = testing_table.remove_columns(["survived"]).shuffle_rows()

split_tuple = titanic.split_rows(0.60) train_table = split_tuple[0] testing_table = split_tuple[1] test_table = testing_table.remove_columns(["survived"]).shuffle_rows()

3. Use OneHotEncoder to create an encoder, that will be used later to transform the training table.

• We use OneHotEncoder to transform non-numerical categorical values into numerical representations with values of zero or one. In this example we will transform the values of the sex column, hence they will be used in the model for predicting the surviving of passengers.
• Use the fit function of the OneHotEncoder to pass the table and the column names, that will be used as features to predict who will survive to the encoder.
• The names of the column before transformation need to be saved, because OneHotEncoder changes the names of the fitted Columns:

In [3]:

from safeds.data.tabular.transformation import OneHotEncoder

old_column_names = train_table.column_names
encoder = OneHotEncoder().fit(train_table, ["sex"])

from safeds.data.tabular.transformation import OneHotEncoder old_column_names = train_table.column_names encoder = OneHotEncoder().fit(train_table, ["sex"])

4. Transform the training table using the fitted encoder, and create a set with the new names of the fitted Columns:

In [4]:

transformed_table = encoder.transform(train_table)
new_column_names = transformed_table.column_names
new_columns= set(new_column_names) - set(old_column_names)

transformed_table = encoder.transform(train_table) new_column_names = transformed_table.column_names new_columns= set(new_column_names) - set(old_column_names)

5. Tag the survived Column as the target variable to be predicted. Use the new names of the fitted Columns as features, which will be used to make predictions based on the target variable.

In [5]:

tagged_train_table= transformed_table.tag_columns("survived", feature_names=[
    *new_columns
])

tagged_train_table= transformed_table.tag_columns("survived", feature_names=[ *new_columns ])

6. Use RandomForest classifier as a model for the classification. Pass the "tagged_titanic" table to the fit function of the model:

In [6]:

from safeds.ml.classical.classification import RandomForest

model = RandomForest()
fitted_model= model.fit(tagged_train_table)

from safeds.ml.classical.classification import RandomForest model = RandomForest() fitted_model= model.fit(tagged_train_table)

7. Use the fitted random forest model, that we trained on the training dataset to predict the survival rate of passengers in the test dataset. Transform the test data with OneHotEncoder first, to be able to pass it to the predict function, that uses our fitted random forest model for prediction:

In [7]:

encoder = OneHotEncoder().fit(test_table, ["sex"])
transformed_test_table = encoder.transform(test_table)

predicition = fitted_model.predict(
    transformed_test_table
)
#For visualisation purposes we only print out the first 15 rows.
predicition.slice_rows(0,15)

encoder = OneHotEncoder().fit(test_table, ["sex"]) transformed_test_table = encoder.transform(test_table) predicition = fitted_model.predict( transformed_test_table ) #For visualisation purposes we only print out the first 15 rows. predicition.slice_rows(0,15)

Out[7]:

	id	name	sex__male	sex__female	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	816	Mock, Mr. Philipp Edmund	1.0	0.0	30.0000	1	0	13236	1	57.7500	C78	Cherbourg	0
1	1134	Somerton, Mr. Francis William	1.0	0.0	30.0000	0	0	A.5. 18509	3	8.0500	NaN	Southampton	0
2	997	Reuchlin, Jonkheer. John George	1.0	0.0	38.0000	0	0	19972	1	0.0000	NaN	Southampton	0
3	1043	Ryan, Mr. Patrick	1.0	0.0	NaN	0	0	371110	3	24.1500	NaN	Queenstown	0
4	1244	Warren, Mr. Charles William	1.0	0.0	NaN	0	0	C.A. 49867	3	7.5500	NaN	Southampton	0
5	1184	Thomas, Master. Assad Alexander	1.0	0.0	0.4167	0	1	2625	3	8.5167	NaN	Cherbourg	0
6	986	Quick, Miss. Phyllis May	0.0	1.0	2.0000	1	1	26360	2	26.0000	NaN	Southampton	1
7	988	Quick, Mrs. Frederick Charles (Jane Richards)	0.0	1.0	33.0000	0	2	26360	2	26.0000	NaN	Southampton	1
8	1063	Sage, Mr. Frederick	1.0	0.0	NaN	8	2	CA. 2343	3	69.5500	NaN	Southampton	0
9	961	Peruschitz, Rev. Joseph Maria	1.0	0.0	41.0000	0	0	237393	2	13.0000	NaN	Southampton	0
10	832	Moss, Mr. Albert Johan	1.0	0.0	NaN	0	0	312991	3	7.7750	NaN	Southampton	0
11	1098	Silvey, Mr. William Baird	1.0	0.0	50.0000	1	0	13507	1	55.9000	E44	Southampton	0
12	981	Ponesell, Mr. Martin	1.0	0.0	34.0000	0	0	250647	2	13.0000	NaN	Southampton	0
13	1290	Windelov, Mr. Einar	1.0	0.0	21.0000	0	0	SOTON/OQ 3101317	3	7.2500	NaN	Southampton	0
14	848	Najib, Miss. Adele Kiamie 'Jane'	0.0	1.0	15.0000	0	0	2667	3	7.2250	NaN	Cherbourg	1

8. You can test the accuracy of that model with the initial testing_table as follows:

In [8]:

encoder = OneHotEncoder().fit(test_table, ["sex"])
testing_table = encoder.transform(testing_table)

tagged_test_table= testing_table.tag_columns("survived", feature_names=[
    *new_columns
])
fitted_model.accuracy(tagged_train_table)

encoder = OneHotEncoder().fit(test_table, ["sex"]) testing_table = encoder.transform(testing_table) tagged_test_table= testing_table.tag_columns("survived", feature_names=[ *new_columns ]) fitted_model.accuracy(tagged_train_table)

Out[8]:

0.7745222929936306