Imiphumela Yemodeli yesi-2: Ukubheka Okujulile Emhlabeni Wokuhlelwa Kwe-Pythonic
I-Python, ulimi lwezinhlelo olusezingeni eliphezulu, olutolikiwe olunezinto, amamojula, izintambo, okuhlukile nokuphathwa kwenkumbulo okuzenzakalelayo, inezinto eziningi ezicebile nemitapo yolwazi esiza ekuxazululeni izinkinga zokuhlela eziyinkimbinkimbi. Ubuhle bePython busebulula bayo futhi imiphumela yemodeli 2 ayihlukile. Kulesi sihloko, sijula emhlabeni wohlelo lwePython, sihlola ubunkimbinkimbi nobunjiniyela bemiphumela yemodeli yesi-2.
Inselele Ngemiphumela Yemodeli 2
Ukuqonda imiphumela yemodeli 2 kungase kubonakale kungenamsebenzi kumhleli wePython ongumakadebona, kodwa kwabaqalayo, kungase kubangele inselele enkulu. Lokhu kwehlela esimweni esiyinkimbinkimbi senkinga lapho imojula ngayinye idinga ukuqonda okujulile kolimi lokuhlela kanye namakhono okucabanga okujulile ukuze kusetshenziswe umtapo wolwazi noma umsebenzi ofanele ukuxazulula inkinga. Kodwa-ke, njenganoma iyiphi enye inselelo yokuhlela, ukuqonda imiqondo ewumongo,
futhi ukuthatha izinyathelo ezihlelekile kungasiza ukunqoba le nselele.
Isixazululo se-Pythonic to Model 2 Outputs
Imiphumela yemodeli 2 kuPython ihlanganisa ukuklama nokusebenzisa imisebenzi nokusebenzisa imitapo yolwazi enikezwa yiPython. Basebenzisa le misebenzi namamojula ukuze bakhe imiphumela, batolike futhi basebenzise idatha.
import numpy as np
from sklearn.linear_model import LinearRegression
#implement Model 2 - Linear Regression
def model_2(x, y):
x = np.array(x).reshape((-1, 1))
model = LinearRegression().fit(x, y)
r_sq = model.score(x, y)
intercept = model.intercept_
slope = model.coef_
return r_sq, intercept, slope
Ukwephula Ikhodi
Okokuqala, singenisa imitapo yolwazi edingekayo- numpy kanye ne-LinearRegression isuka ku-sklearn.linear_model. I-Numpy iwumtapo wezincwadi onamandla owengeza ukusekela kwamalungu afanayo anezinhlangothi eziningi namatrices, kanye nenhlobonhlobo ebanzi yemisebenzi yezibalo ukuze isebenzise lawa malungu.
- Umsebenzi we-“model_2” uthatha amapharamitha amabili, 'x' kanye 'y'.
- Idatha ye-'x' ibunjwe kabusha yaba uhlu lwe-2D oludingekayo ekusebenzeni kokulingana.
- Umsebenzi othi 'fit' ubizwa ukuze ulingane nemodeli yomugqa wokuhlehla.
- Okokugcina, sibuyisela i-coefficient of determination (r_sq), imigomo yokunqamula neyemithambeka yemodeli.
Ukuqonda Amalabhulali Nemisebenzi kulo Mongo
Kumazwibela ethu ekhodi yePython, sisebenzisa i-numpy ne-LinearRegression. I-Numpy, enezakhiwo zayo ezinamandla zedatha, yengeza emandleni ePython ekubalweni kwezibalo. Isiza ekuphatheni amalungu afanayo njengesakhiwo sedatha eyisisekelo futhi ithuthukisa ukusebenza kahle lapho kwenziwa izibalo ezihlanganisiwe kuzo. Lokhu kuyenza ifaneleke ngokuphelele ukuphatha idatha yethu yokufaka 'x' kanye 'y'.
Ukuhlehla Okuqondile , kanye nekhono layo lokumelela ubudlelwano obuyinkimbinkimbi, kwenza kube lula ukuthola izitayela namaphethini kudatha. Ihlola ubudlelwano obuqondile phakathi kwedatha yokufaka neyokukhipha ngenkathi inciphisa inani lezinsalela eziyisikwele.
Ngokuqonda okugcwele inkinga, ikhodi, nemisebenzi ehilelekile, iPython iveza indlela eqondile kodwa ephumelelayo ekubhekaneni Nemiphumela Yemodeli Yesibili. Ngokusebenzisa amakhono abanzi ePython, izinselelo ezifana nalezi ziba amathuba okuqamba izinto ezintsha nokufunda.
