Fazendo previsões com interações
Regressão Intermediária com statsmodels em Python
Maarten Van den Broeck
Content Developer at DataCamp
O modelo com interação
mdl_mass_vs_both_inter = ols("mass_g ~ species + species:length_cm + 0",
data=fish).fit()
print(mdl_mass_vs_both_inter.params)
species[Bream] -1035.3476
species[Perch] -619.1751
species[Pike] -1540.8243
species[Roach] -329.3762
species[Bream]:length_cm 54.5500
species[Perch]:length_cm 38.9115
species[Pike]:length_cm 53.1949
species[Roach]:length_cm 23.3193
O fluxo de previsão
from itertools import product
length_cm = np.arange(5, 61, 5)
species = fish["species"].unique()
p = product(length_cm, species)
explanatory_data = pd.DataFrame(p,
columns=["length_cm",
"species"])
prediction_data = explanatory_data.assign(
mass_g = mdl_mass_vs_both_inter.predict(explanatory_data))
print(prediction_data)
length_cm species mass_g
0 5 Bream -762.5977
1 5 Roach -212.7799
2 5 Perch -424.6178
3 5 Pike -1274.8499
4 10 Bream -489.8478
5 10 Roach -96.1836
6 10 Perch -230.0604
7 10 Pike -1008.8756
8 15 Bream -217.0979
...
40 55 Bream 1964.9014
41 55 Roach 953.1833
42 55 Perch 1520.9556
43 55 Pike 1384.8933
44 60 Bream 2237.6513
45 60 Roach 1069.7796
46 60 Perch 1715.5129
47 60 Pike 1650.8677
Visualizando as previsões
sns.lmplot(x="length_cm",
y="mass_g",
data=fish,
hue="species",
ci=None)
sns.scatterplot(x="length_cm",
y="mass_g",
data=prediction_data,
hue="species")
plt.show()
Calculando as previsões manualmente
coeffs = mdl_mass_vs_both_inter.params
species[Bream] -1035.3476
species[Perch] -619.1751
species[Pike] -1540.8243
species[Roach] -329.3762
species[Bream]:length_cm 54.5500
species[Perch]:length_cm 38.9115
species[Pike]:length_cm 53.1949
species[Roach]:length_cm 23.3193
ic_bream, ic_perch, ic_pike, ic_roach,
slope_bream, slope_perch, slope_pike, slope_roach = coeffs
Calculando as previsões manualmente
conditions = [ explanatory_data["species"] == "Bream", explanatory_data["species"] == "Perch", explanatory_data["species"] == "Pike", explanatory_data["species"] == "Roach" ]ic_choices = [ic_bream, ic_perch, ic_pike, ic_roach]intercept = np.select(conditions, ic_choices)slope_choices = [slope_bream, slope_perch, slope_pike, slope_roach]slope = np.select(conditions, slope_choices)
Calculando as previsões manualmente
prediction_data = explanatory_data.assign(
mass_g = intercept + slope * explanatory_data["length_cm"])
print(prediction_data)
length_cm species mass_g
0 5 Bream -762.5977
1 5 Roach -212.7799
2 5 Perch -424.6178
3 5 Pike -1274.8499
4 10 Bream -489.8478
5 10 Roach -96.1836
...
43 55 Pike 1384.8933
44 60 Bream 2237.6513
45 60 Roach 1069.7796
46 60 Perch 1715.5129
47 60 Pike 1650.8677
prediction_data = explanatory_data.assign(
mass_g = mdl_mass_vs_both_inter.predict(explanatory_data))
print(prediction_data)
length_cm species mass_g
0 5 Bream -762.5977
1 5 Roach -212.7799
2 5 Perch -424.6178
3 5 Pike -1274.8499
4 10 Bream -489.8478
5 10 Roach -96.1836
...
43 55 Pike 1384.8933
44 60 Bream 2237.6513
45 60 Roach 1069.7796
46 60 Perch 1715.5129
47 60 Pike 1650.8677
Vamos praticar!
Regressão Intermediária com statsmodels em Python
