Why create unnecessary variables in modeling (Lab-2)

Course Q&A Machine Learning Specialization Advanced Learning Algorithms
1

For Lab-2 –

Why is there a need to create 2 variables for Sex and Exercise Angina?

For instance, for Sex - 0 could have been Male and 1 could have been female.

Age: age of the patient [years]
Sex: sex of the patient [M: Male, F: Female]
ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
RestingBP: resting blood pressure [mm Hg]
Cholesterol: serum cholesterol [mm/dl]
FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]
MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]
ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
Oldpeak: oldpeak = ST [Numeric value measured in depression]
ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
HeartDisease: output class [1: heart disease, 0: Normal]
2

Hi @rkranjan

I can not find this lab in MLS Course2 week4. Could you please check and post to the correct discussion forum.

3

I agree with mentor Kic.

In general, categories are converted to one-hot encoding. If there are only two categories, this step could be skipped.