{"id":44730,"date":"2026-09-16T16:00:57","date_gmt":"2026-09-16T13:00:57","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-dunyasina-scikit-learn-ile-hizli-bir-giris-yapmak-mumkun-mu\/"},"modified":"2026-09-16T16:00:57","modified_gmt":"2026-09-16T13:00:57","slug":"makine-ogrenmesi-dunyasina-scikit-learn-ile-hizli-bir-giris-yapmak-mumkun-mu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-dunyasina-scikit-learn-ile-hizli-bir-giris-yapmak-mumkun-mu\/","title":{"rendered":"Makine \u00d6\u011frenmesi D\u00fcnyas\u0131na Scikit-learn ile H\u0131zl\u0131 Bir Giri\u015f Yapmak M\u00fcmk\u00fcn M\u00fc?"},"content":{"rendered":"
\n

Yapay zeka ve makine \u00f6\u011frenmesi r\u00fczgar\u0131 esiyor, de\u011fil mi? Her g\u00fcn yeni bir makale, yeni bir ara\u00e7, yeni bir ba\u015far\u0131 hikayesiyle kar\u015f\u0131la\u015f\u0131yoruz. Peki, bu heyecan verici alana ad\u0131m atmak i\u00e7in illa ki karma\u015f\u0131k matematiksel form\u00fcllere bo\u011fulmak, saatlerce algoritma teorisi \u00e7al\u0131\u015fmak zorunda m\u0131y\u0131z? Elbette hay\u0131r! \u00d6zellikle yaz\u0131l\u0131m geli\u015ftirme ge\u00e7mi\u015fi olanlar i\u00e7in makine \u00f6\u011frenmesi modelleri kurmak, d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fcn\u00fczden \u00e7ok daha eri\u015filebilir. Bu rehberde, Python’\u0131n en pop\u00fcler makine \u00f6\u011frenmesi k\u00fct\u00fcphanesi olan Scikit-learn ile s\u0131f\u0131rdan, anla\u015f\u0131l\u0131r bir \u015fekilde nas\u0131l basit bir model olu\u015fturabilece\u011finizi ke\u015ffedece\u011fiz. \u0130ster stajyer olun, ister tak\u0131m lideri, ister bu alana yeni merak salm\u0131\u015f biri; bu yolculukta size rehberlik etmek i\u00e7in buraday\u0131z. Haz\u0131rsan\u0131z, kodlama maceras\u0131na ba\u015flayal\u0131m!<\/p>\n

Temel Kavramlar: Makine \u00d6\u011frenmesi Nedir ve Neden Scikit-learn?<\/h2>\n

Makine \u00f6\u011frenmesi, bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan, verilerden \u00f6\u011frenmesini sa\u011flayan bir yapay zeka dal\u0131d\u0131r. D\u00fc\u015f\u00fcn\u00fcn ki bir \u00e7ocu\u011fa kedileri ve k\u00f6pekleri \u00f6\u011fretmek istiyorsunuz. Ona bir s\u00fcr\u00fc kedi ve k\u00f6pek resmi g\u00f6sterir, “Bu kedi”, “Bu k\u00f6pek” dersiniz. Zamanla \u00e7ocuk, kedinin ve k\u00f6pe\u011fin \u00f6zelliklerini \u00f6\u011frenerek yeni g\u00f6rd\u00fc\u011f\u00fc bir hayvan\u0131n kedi mi yoksa k\u00f6pek mi oldu\u011funu tahmin edebilir hale gelir. Makine \u00f6\u011frenmesi de temelde benzer bir prensiple \u00e7al\u0131\u015f\u0131r: Algoritmalar, bol miktarda veriyle beslenerek bu verilerdeki \u00f6r\u00fcnt\u00fcleri ve ili\u015fkileri \u00f6\u011frenir ve bu bilgiyi kullanarak gelecekteki veriler hakk\u0131nda tahminlerde bulunur veya kararlar al\u0131r.<\/p>\n

Makine \u00f6\u011frenmesi denince akla bir\u00e7ok farkl\u0131 algoritma ve teknik gelir. Bunlar genel olarak iki ana kategoriye ayr\u0131l\u0131r: G\u00f6zetimli \u00d6\u011frenme (Supervised Learning) ve G\u00f6zetimsiz \u00d6\u011frenme (Unsupervised Learning). G\u00f6zetimli \u00f6\u011frenmede, algoritmaya hem girdi verisi hem de bu girdilere kar\u015f\u0131l\u0131k gelen do\u011fru \u00e7\u0131kt\u0131lar (etiketler) verilir. Ama\u00e7, girdi ve \u00e7\u0131kt\u0131 aras\u0131ndaki ili\u015fkiyi \u00f6\u011frenerek yeni girdiler i\u00e7in do\u011fru \u00e7\u0131kt\u0131lar\u0131 tahmin etmektir. \u00d6rne\u011fin, ev fiyatlar\u0131n\u0131 tahmin etmek i\u00e7in evin \u00f6zelliklerini (metrekare, oda say\u0131s\u0131 vb.) ve o evin ger\u00e7ek sat\u0131\u015f fiyat\u0131n\u0131 i\u00e7eren verilerle bir model e\u011fitilebilir. G\u00f6zetimsiz \u00f6\u011frenmede ise algoritmaya sadece girdi verisi verilir ve algoritmadan bu verilerdeki gizli yap\u0131lar\u0131, gruplar\u0131 veya \u00f6r\u00fcnt\u00fcleri ke\u015ffetmesi beklenir. K\u00fcmeleme (clustering) ve boyut indirgeme (dimensionality reduction) gibi teknikler bu kategoriye girer.<\/p>\n

Peki, neden Scikit-learn? Python ekosisteminde makine \u00f6\u011frenmesi alan\u0131nda \u00f6ne \u00e7\u0131kan bir\u00e7ok k\u00fct\u00fcphane olsa da Scikit-learn, sundu\u011fu geni\u015f fonksiyonellik, kullan\u0131m kolayl\u0131\u011f\u0131 ve g\u00fc\u00e7l\u00fc topluluk deste\u011fi ile \u00f6ne \u00e7\u0131kar. Scikit-learn, makine \u00f6\u011frenmesinin temel g\u00f6revlerini yerine getirmek i\u00e7in gerekli t\u00fcm ara\u00e7lar\u0131 tek bir \u00e7at\u0131 alt\u0131nda toplar: veri \u00f6n i\u015fleme, \u00f6zellik m\u00fchendisli\u011fi, model se\u00e7imi, model e\u011fitimi ve model de\u011ferlendirme. \u00dcstelik, bu ara\u00e7lar olduk\u00e7a iyi belgelenmi\u015f ve anla\u015f\u0131l\u0131r bir API’ye sahiptir. Bu da onu hem yeni ba\u015flayanlar hem de deneyimli geli\u015ftiriciler i\u00e7in ideal bir se\u00e7im haline getirir. Scikit-learn’\u00fcn en b\u00fcy\u00fck avantajlar\u0131ndan biri de farkl\u0131 algoritmalar\u0131n ayn\u0131 standart aray\u00fcz\u00fc kullanmas\u0131d\u0131r. Bu sayede, bir algoritmay\u0131 kullanmay\u0131 \u00f6\u011frendi\u011finizde, di\u011ferlerini de kolayca benimseyebilirsiniz. Bu, \u00f6zellikle farkl\u0131 modelleri kar\u015f\u0131la\u015ft\u0131rmak ve projeniz i\u00e7in en uygun olan\u0131 bulmak istedi\u011finizde b\u00fcy\u00fck bir h\u0131z kazand\u0131r\u0131r.<\/p>\n

Haz\u0131rl\u0131k: Gerekli Ara\u00e7lar\u0131 Kurmak ve Veri Seti Se\u00e7imi<\/h2>\n

Herhangi bir kodlama projesinde oldu\u011fu gibi, makine \u00f6\u011frenmesi projelerinde de do\u011fru ara\u00e7lara sahip olmak i\u015fleri b\u00fcy\u00fck \u00f6l\u00e7\u00fcde kolayla\u015ft\u0131r\u0131r. Scikit-learn ile \u00e7al\u0131\u015fmaya ba\u015flamak i\u00e7in bilgisayar\u0131n\u0131zda Python’\u0131n kurulu olmas\u0131 gerekmektedir. E\u011fer hen\u00fcz kurulu de\u011filse, Python’\u0131n resmi web sitesinden (python.org) en son s\u00fcr\u00fcm\u00fc indirip kurabilirsiniz. Python kurulumu s\u0131ras\u0131nda “Add Python to PATH” se\u00e7ene\u011fini i\u015faretlemeyi unutmay\u0131n; bu, komut sat\u0131r\u0131ndan Python’\u0131 daha kolay \u00e7al\u0131\u015ft\u0131rman\u0131z\u0131 sa\u011flar.<\/p>\n

Python’\u0131 kurduktan sonra, makine \u00f6\u011frenmesi projelerinde s\u0131k\u00e7a kullan\u0131lan baz\u0131 temel k\u00fct\u00fcphaneleri de y\u00fcklememiz gerekiyor. Bunlar aras\u0131nda veri manip\u00fclasyonu ve analizi i\u00e7in NumPy<\/strong> ve Pandas<\/strong>, veri g\u00f6rselle\u015ftirme i\u00e7in Matplotlib<\/strong> ve Seaborn<\/strong>, ve tabii ki makine \u00f6\u011frenmesi algoritmalar\u0131 i\u00e7in Scikit-learn<\/strong> bulunmaktad\u0131r. Bu k\u00fct\u00fcphaneleri y\u00fcklemenin en kolay yolu, Python’\u0131n paket y\u00f6neticisi olan pip’i kullanmakt\u0131r. Komut istemcisini veya terminali a\u00e7\u0131n ve a\u015fa\u011f\u0131daki komutlar\u0131 s\u0131ras\u0131yla \u00e7al\u0131\u015ft\u0131r\u0131n:<\/p>\n

\n  pip install numpy pandas matplotlib seaborn scikit-learn jupyter\n  <\/code><\/pre>\n
Jupyter Notebook<\/strong> veya JupyterLab<\/strong>‘\u0131 da ekledik. Bu interaktif geli\u015ftirme ortamlar\u0131, kodunuzu h\u00fccreler halinde yazman\u0131za, \u00e7al\u0131\u015ft\u0131rman\u0131za ve sonu\u00e7lar\u0131 an\u0131nda g\u00f6rmenize olanak tan\u0131r. Bu, \u00f6zellikle veri analizi ve model geli\u015ftirme s\u00fcre\u00e7lerinde b\u00fcy\u00fck kolayl\u0131k sa\u011flar. Jupyter’\u0131 kurduktan sonra, bir klas\u00f6rde komut istemcisini a\u00e7\u0131p jupyter notebook<\/code> komutunu \u00e7al\u0131\u015ft\u0131rarak web tabanl\u0131 aray\u00fcz\u00fcn\u00fc ba\u015flatabilirsiniz.<\/p>\n
\u015eimdi s\u0131ra geldi veri setine. Makine \u00f6\u011frenmesi modellerini e\u011fitmek i\u00e7in ger\u00e7ek verilere ihtiyac\u0131m\u0131z var. Yeni ba\u015flayanlar i\u00e7in, Scikit-learn ile birlikte gelen \u00f6rnek veri setleri harika bir ba\u015flang\u0131\u00e7 noktas\u0131d\u0131r. Bu veri setleri, ger\u00e7ek d\u00fcnya problemlerini basitle\u015ftirilmi\u015f bir \u015fekilde temsil eder ve farkl\u0131 makine \u00f6\u011frenmesi g\u00f6revlerini denemek i\u00e7in idealdir. \u00d6rne\u011fin, Iris veri seti<\/strong> \u00e7i\u00e7ek t\u00fcrlerini s\u0131n\u0131fland\u0131rmak i\u00e7in, Digits veri seti<\/strong> el yaz\u0131s\u0131 rakamlar\u0131 tan\u0131mak i\u00e7in, Boston Housing veri seti<\/strong> ise ev fiyatlar\u0131n\u0131 tahmin etmek i\u00e7in kullan\u0131l\u0131r.<\/p>\n
Bu rehberde, daha anla\u015f\u0131l\u0131r bir \u00f6rnek olmas\u0131 a\u00e7\u0131s\u0131ndan Iris veri setini<\/strong> kullanaca\u011f\u0131z. Iris veri seti, \u00fc\u00e7 farkl\u0131 Iris \u00e7i\u00e7e\u011fi t\u00fcr\u00fcn\u00fcn (setosa, versicolor, virginica) ta\u00e7 yaprak ve \u00e7anak yaprak uzunluklar\u0131 ve geni\u015flikleri gibi \u00f6zelliklerini i\u00e7erir. Amac\u0131m\u0131z, bu \u00f6zelliklere bakarak \u00e7i\u00e7e\u011fin hangi t\u00fcre ait oldu\u011funu tahmin eden bir model olu\u015fturmak olacakt\u0131r. Veri setini Scikit-learn’den \u015fu \u015fekilde y\u00fckleyebilirsiniz:<\/p>\n
\n  from sklearn.datasets import load_iris\n  iris = load_iris()\n  X = iris.data  # \u00d6zellikler (features)\n  y = iris.target # Hedef de\u011fi\u015fken (target)\n  <\/code><\/pre>\n
Burada X<\/code>, \u00e7i\u00e7e\u011fin \u00f6zelliklerini i\u00e7eren bir NumPy dizisidir (\u00f6rne\u011fin, ta\u00e7 yaprak uzunlu\u011fu, ta\u00e7 yaprak geni\u015fli\u011fi vb.), y<\/code> ise her bir \u00e7i\u00e7e\u011fin t\u00fcr\u00fcn\u00fc temsil eden etiketleri i\u00e7eren bir NumPy dizisidir. Bu verileri kullanarak ilk modelimizi olu\u015fturmaya haz\u0131r\u0131z!<\/p>\n
\u0130lk Modelimiz: Veri Setini Haz\u0131rlama ve Modeli E\u011fitme<\/h2>\n
Veri setimizi y\u00fckledik, \u015fimdi s\u0131ra geldi onu makine \u00f6\u011frenmesi modelimizin anlayabilece\u011fi bir formata getirmeye ve ard\u0131ndan modeli e\u011fitmeye. Bu a\u015famada veri \u00f6n i\u015fleme (data preprocessing) ve model e\u011fitimi (model training) ad\u0131mlar\u0131n\u0131 ger\u00e7ekle\u015ftirece\u011fiz. Unutmay\u0131n, makine \u00f6\u011frenmesinde verinin kalitesi ve do\u011fru haz\u0131rl\u0131\u011f\u0131, modelin ba\u015far\u0131s\u0131 \u00fczerinde do\u011frudan etkilidir. K\u00f6t\u00fc veri, k\u00f6t\u00fc model demektir!<\/p>\n
\u0130lk ad\u0131m\u0131m\u0131z, veri setini e\u011fitim (training) ve test (testing) setlerine ay\u0131rmak. Bu, modelimizin daha \u00f6nce hi\u00e7 g\u00f6rmedi\u011fi veriler \u00fczerinde ne kadar iyi performans g\u00f6sterdi\u011fini anlamam\u0131z\u0131 sa\u011flar. E\u011fer t\u00fcm veriyi modelin e\u011fitiminde kullan\u0131rsak, modelin ezberleme olas\u0131l\u0131\u011f\u0131 artar ve ger\u00e7ek d\u00fcnyadaki genelleme yetene\u011fi hakk\u0131nda yan\u0131lt\u0131c\u0131 bilgi elde ederiz. Scikit-learn, bu ay\u0131rma i\u015flemini kolayla\u015ft\u0131ran train_test_split<\/code> fonksiyonunu sunar:<\/p>\n
\n  from sklearn.model_selection import train_test_split\n\n  # Veri setini %80 e\u011fitim, %20 test olarak ay\u0131r\u0131yoruz\n  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n  <\/code><\/pre>\n
Burada test_size=0.2<\/code> parametresi, veri setinin %20’sinin test i\u00e7in ayr\u0131laca\u011f\u0131n\u0131 belirtir. random_state<\/code> ise, her \u00e7al\u0131\u015ft\u0131rmada ayn\u0131 rastgele b\u00f6lme i\u015fleminin ger\u00e7ekle\u015fmesini sa\u011flar, bu da sonu\u00e7lar\u0131n tekrarlanabilirli\u011fi a\u00e7\u0131s\u0131ndan \u00f6nemlidir. E\u011fer random_state<\/code> belirtmezseniz, her \u00e7al\u0131\u015ft\u0131rmada farkl\u0131 bir b\u00f6lme i\u015flemi ger\u00e7ekle\u015fir.<\/p>\n
Veri setini b\u00f6ld\u00fc\u011f\u00fcm\u00fcze g\u00f6re, \u015fimdi ilk makine \u00f6\u011frenmesi modelimizi se\u00e7ebilir ve e\u011fitebiliriz. Iris veri seti s\u0131n\u0131fland\u0131rma problemi oldu\u011fu i\u00e7in, basit bir s\u0131n\u0131fland\u0131rma algoritmas\u0131 olan Karar A\u011fa\u00e7lar\u0131 (Decision Trees)<\/strong>‘n\u0131 kullanabiliriz. Karar a\u011fa\u00e7lar\u0131, verileri belirli \u00f6zelliklere g\u00f6re dallara ay\u0131rarak bir karar verme s\u00fcreci izler, bu da anla\u015f\u0131lmas\u0131n\u0131 kolayla\u015ft\u0131r\u0131r.<\/p>\n
Scikit-learn’de Karar A\u011fa\u00e7lar\u0131 s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131 DecisionTreeClassifier<\/code> olarak bulunur. Modeli e\u011fitmek i\u00e7in \u015fu ad\u0131mlar\u0131 izleriz:<\/p>\n
\n  from sklearn.tree import DecisionTreeClassifier\n\n  # Karar A\u011fac\u0131 s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131n\u0131 olu\u015fturuyoruz\n  model = DecisionTreeClassifier(random_state=42)\n\n  # Modeli e\u011fitim verileriyle e\u011fitiyoruz\n  model.fit(X_train, y_train)\n  <\/code><\/pre>\n
\u0130\u015fte bu kadar! model.fit(X_train, y_train)<\/code> komutu ile modelimiz, e\u011fitim verilerindeki \u00f6r\u00fcnt\u00fcleri \u00f6\u011frenmi\u015f oldu. model<\/code> nesnesi art\u0131k \u00e7i\u00e7e\u011fin \u00f6zelliklerini girdi olarak ald\u0131\u011f\u0131nda hangi t\u00fcre ait olaca\u011f\u0131n\u0131 tahmin etmeye haz\u0131rd\u0131r. Bu kadar k\u0131sa s\u00fcrede, veri setini haz\u0131rlay\u0131p bir makine \u00f6\u011frenmesi modelini e\u011fitmi\u015f olduk. Bu, Scikit-learn’\u00fcn ne kadar g\u00fc\u00e7l\u00fc ve kullan\u0131c\u0131 dostu oldu\u011funun bir kan\u0131t\u0131d\u0131r.<\/p>\n
Model Performans\u0131n\u0131 De\u011ferlendirme: Do\u011fruluk ve \u00d6tesi<\/h2>\n
Bir makine \u00f6\u011frenmesi modeli olu\u015fturmak harika, peki bu modelin ne kadar iyi \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 nas\u0131l anlar\u0131z? \u0130\u015fte burada model de\u011ferlendirme (model evaluation) devreye giriyor. Modelimizin performans\u0131n\u0131 \u00f6l\u00e7mek i\u00e7in daha \u00f6nce ay\u0131rd\u0131\u011f\u0131m\u0131z test setini<\/strong> kullanaca\u011f\u0131z. Test setindeki verileri modele verip, modelin tahminlerinin ger\u00e7ek etiketlerle ne kadar uyumlu oldu\u011funu kar\u015f\u0131la\u015ft\u0131raca\u011f\u0131z.<\/p>\n
En temel ve yayg\u0131n kullan\u0131lan de\u011ferlendirme metriklerinden biri do\u011fruluk (accuracy)<\/strong>‘tur. Do\u011fruluk, modelin t\u00fcm tahminleri i\u00e7indeki do\u011fru tahminlerin oran\u0131n\u0131 ifade eder. Scikit-learn’de accuracy_score<\/code> fonksiyonu bu hesaplamay\u0131 kolayla\u015ft\u0131r\u0131r:<\/p>\n
\n  from sklearn.metrics import accuracy_score\n\n  # Test verileri \u00fczerinde tahminler yap\u0131yoruz\n  y_pred = model.predict(X_test)\n\n  # Do\u011frulu\u011fu hesapl\u0131yoruz\n  accuracy = accuracy_score(y_test, y_pred)\n  print(f\"Modelin Do\u011frulu\u011fu: {accuracy:.2f}\")\n  <\/code><\/pre>\n
Bu kod blo\u011fu, model.predict(X_test)<\/code> ile test setindeki \u00e7i\u00e7eklerin t\u00fcrlerini tahmin eder ve ard\u0131ndan accuracy_score<\/code> fonksiyonu ile bu tahminlerin ger\u00e7ek etiketlerle (y_test<\/code>) ne kadar e\u015fle\u015fti\u011fini hesaplar. Elde etti\u011fimiz do\u011fruluk oran\u0131, modelimizin genel olarak ne kadar ba\u015far\u0131l\u0131 oldu\u011funu g\u00f6sterir. Ancak, sadece do\u011frulu\u011fa bakmak bazen yan\u0131lt\u0131c\u0131 olabilir, \u00f6zellikle de veri setindeki s\u0131n\u0131flar dengesiz oldu\u011funda.<\/p>\n
Daha derinlemesine bir analiz i\u00e7in karma\u015f\u0131kl\u0131k matrisi (confusion matrix)<\/strong> ve s\u0131n\u0131fland\u0131rma raporu (classification report)<\/strong> gibi metrikler de kullan\u0131l\u0131r. Karma\u015f\u0131kl\u0131k matrisi, modelin hangi s\u0131n\u0131flar\u0131 do\u011fru tahmin etti\u011fini, hangi s\u0131n\u0131flar\u0131 birbiriyle kar\u0131\u015ft\u0131rd\u0131\u011f\u0131n\u0131 detayl\u0131 bir \u015fekilde g\u00f6sterir. S\u0131n\u0131fland\u0131rma raporu ise her bir s\u0131n\u0131f i\u00e7in hassasiyet (precision), geri \u00e7a\u011f\u0131rma (recall) ve F1-skoru gibi metrikleri sunar. Bu metrikler, modelin her bir s\u0131n\u0131f i\u00e7in ne kadar iyi performans g\u00f6sterdi\u011fini anlamam\u0131za yard\u0131mc\u0131 olur.<\/p>\n
\n  from sklearn.metrics import confusion_matrix, classification_report\n\n  # Karma\u015f\u0131kl\u0131k matrisini yazd\u0131r\n  print(\"\\nKarma\u015f\u0131kl\u0131k Matrisi:\")\n  print(confusion_matrix(y_test, y_pred))\n\n  # S\u0131n\u0131fland\u0131rma raporunu yazd\u0131r\n  print(\"\\nS\u0131n\u0131fland\u0131rma Raporu:\")\n  print(classification_report(y_test, y_pred, target_names=iris.target_names))\n  <\/code><\/pre>\n
Karma\u015f\u0131kl\u0131k matrisini yorumlarken, k\u00f6\u015fegen \u00fczerindeki de\u011ferlerin do\u011fru tahminleri, di\u011fer de\u011ferlerin ise yanl\u0131\u015f s\u0131n\u0131fland\u0131rmalar\u0131 temsil etti\u011fini g\u00f6rebilirsiniz. S\u0131n\u0131fland\u0131rma raporundaki precision<\/code> (hassasiyet), modelin pozitif olarak tahmin etti\u011fi \u00f6rneklerin ne kadar\u0131n\u0131n ger\u00e7ekten pozitif oldu\u011funu; recall<\/code> (geri \u00e7a\u011f\u0131rma) ise ger\u00e7ek pozitif \u00f6rneklerin ne kadar\u0131n\u0131n model taraf\u0131ndan do\u011fru bir \u015fekilde pozitif olarak tespit edildi\u011fini g\u00f6sterir. f1-score<\/code> ise precision ve recall’un harmonik ortalamas\u0131d\u0131r ve dengeli bir performans \u00f6l\u00e7\u00fct\u00fc sunar.<\/p>\n
Bu metrikler sayesinde, modelimizin sadece genel olarak ba\u015far\u0131l\u0131 olup olmad\u0131\u011f\u0131n\u0131 de\u011fil, ayn\u0131 zamanda hangi s\u0131n\u0131flarda zorland\u0131\u011f\u0131n\u0131 da anlayabiliriz. Bu bilgiler, modelimizi iyile\u015ftirmek i\u00e7in bize yol g\u00f6sterecektir.<\/p>\n
Model \u0130yile\u015ftirme Teknikleri: Daha \u0130yi Performans \u0130\u00e7in Neler Yapabiliriz?<\/h2>\n
\u0130lk modelimizi e\u011fittik ve performans\u0131n\u0131 de\u011ferlendirdik. Peki ya sonu\u00e7lar bekledi\u011fimiz gibi de\u011filse veya daha da iyi bir performans elde etmek istiyorsak ne yapmal\u0131y\u0131z? Makine \u00f6\u011frenmesi modellerini iyile\u015ftirmek, iteratif bir s\u00fcre\u00e7tir ve bir\u00e7ok farkl\u0131 teknik bar\u0131nd\u0131r\u0131r. Bu b\u00f6l\u00fcmde, modelimizin performans\u0131n\u0131 art\u0131rmak i\u00e7in kullanabilece\u011fimiz baz\u0131 temel y\u00f6ntemlere de\u011finece\u011fiz.<\/p>\n
\u0130lk olarak, hiperparametre ayar\u0131 (hyperparameter tuning)<\/strong> \u00e7ok \u00f6nemlidir. Kulland\u0131\u011f\u0131m\u0131z algoritmalar\u0131n, \u00f6rne\u011fin Karar A\u011fa\u00e7lar\u0131’n\u0131n, performans\u0131 \u00fczerinde etkili olan baz\u0131 ayarlanabilir parametreleri vard\u0131r. Bu parametreler, modelin e\u011fitim s\u00fcreci s\u0131ras\u0131nda \u00f6\u011frenilmez, \u00f6nceden bizim taraf\u0131m\u0131zdan belirlenir. Karar A\u011fa\u00e7lar\u0131 i\u00e7in max_depth<\/code> (a\u011fac\u0131n maksimum derinli\u011fi), min_samples_split<\/code> (bir d\u00fc\u011f\u00fcm\u00fc ay\u0131rmak i\u00e7in gereken minimum \u00f6rnek say\u0131s\u0131) gibi parametreler bunlardan baz\u0131lar\u0131d\u0131r. Bu hiperparametreleri do\u011fru \u015fekilde ayarlayarak modelin a\u015f\u0131r\u0131 uyumunu (overfitting) \u00f6nleyebilir veya eksik uyumu (underfitting) giderebiliriz.<\/p>\n
Scikit-learn, hiperparametre ayar\u0131 i\u00e7in Grid Search<\/strong> ve Random Search<\/strong> gibi ara\u00e7lar sunar. Grid Search, belirtilen parametre de\u011ferlerinin t\u00fcm olas\u0131 kombinasyonlar\u0131n\u0131 deneyerek en iyi performans\u0131 veren seti bulmaya \u00e7al\u0131\u015f\u0131r. Random Search ise rastgele kombinasyonlar deneyerek daha h\u0131zl\u0131 sonu\u00e7 verebilir.<\/p>\n
\n  from sklearn.model_selection import GridSearchCV\n\n  # Denemek istedi\u011fimiz hiperparametreler ve de\u011ferleri\n  param_grid = {\n      'max_depth': [3, 5, 7, 10, None],\n      'min_samples_split': [2, 5, 10],\n      'min_samples_leaf': [1, 2, 4]\n  }\n\n  # GridSearchCV nesnesini olu\u015fturuyoruz\n  grid_search = GridSearchCV(DecisionTreeClassifier(random_state=42), param_grid, cv=5) # cv=5, 5 katl\u0131 \u00e7apraz do\u011frulama kullan\u0131r\n\n  # Grid Search'\u00fc e\u011fitim verileri \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131yoruz\n  grid_search.fit(X_train, y_train)\n\n  # En iyi parametreleri ve en iyi skoru yazd\u0131r\u0131yoruz\n  print(f\"En iyi parametreler: {grid_search.best_params_}\")\n  print(f\"En iyi skor (do\u011fruluk): {grid_search.best_score_:.2f}\")\n\n  # En iyi modeli al\u0131yoruz\n  best_model = grid_search.best_estimator_\n  <\/code><\/pre>\n
Bu kod, Karar A\u011fac\u0131 i\u00e7in farkl\u0131 max_depth<\/code>, min_samples_split<\/code> ve min_samples_leaf<\/code> de\u011ferlerini deneyerek en iyi kombinasyonu bulur. cv=5<\/code> ile 5 katl\u0131 \u00e7apraz do\u011frulama (cross-validation) kullan\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6r\u00fcyoruz. \u00c7apraz do\u011frulama, veri setini birden fazla par\u00e7aya b\u00f6l\u00fcp modeli farkl\u0131 par\u00e7alar \u00fczerinde e\u011fitmeyi ve test etmeyi sa\u011flar, bu da modelin genelleme yetene\u011fi hakk\u0131nda daha g\u00fcvenilir bir tahmin sunar.<\/p>\n
Hiperparametre ayar\u0131n\u0131n yan\u0131 s\u0131ra, \u00f6zellik m\u00fchendisli\u011fi (feature engineering)<\/strong> de model performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkileyebilir. Mevcut \u00f6zelliklerden yeni, daha bilgilendirici \u00f6zellikler t\u00fcretmek veya gereksiz \u00f6zellikleri kald\u0131rmak modelin \u00f6\u011frenme yetene\u011fini art\u0131rabilir. \u00d6rne\u011fin, iki \u00f6zelli\u011fin oran\u0131n\u0131 veya toplam\u0131n\u0131 yeni bir \u00f6zellik olarak eklemek faydal\u0131 olabilir.<\/p>\n
Ayr\u0131ca, farkl\u0131 algoritmalar\u0131 denemek<\/strong> de ak\u0131ll\u0131ca bir stratejidir. Her algoritman\u0131n kendine \u00f6zg\u00fc g\u00fc\u00e7l\u00fc ve zay\u0131f y\u00f6nleri vard\u0131r. Bir problem i\u00e7in Karar A\u011fa\u00e7lar\u0131 iyi \u00e7al\u0131\u015fm\u0131yorsa, Lojistik Regresyon, Destek Vekt\u00f6r Makineleri (SVM), Rastgele Ormanlar (Random Forest) veya Gradient Boosting gibi di\u011fer algoritmalar\u0131 deneyebilirsiniz. Scikit-learn’\u00fcn sundu\u011fu geni\u015f algoritma yelpazesi sayesinde bu ge\u00e7i\u015f olduk\u00e7a kolayd\u0131r.<\/p>\n
Son olarak, veri setinin b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc art\u0131rmak<\/strong> veya veri art\u0131rma (data augmentation)<\/strong> tekniklerini kullanmak da performans\u0131 iyile\u015ftirebilir, ancak bu her zaman m\u00fcmk\u00fcn olmayabilir.<\/p>\n
Bu teknikleri kullanarak, basit bir ba\u015flang\u0131\u00e7 modelinden \u00e7ok daha g\u00fc\u00e7l\u00fc ve do\u011fru bir modele ula\u015fabiliriz. \u00d6nemli olan, denemekten ve farkl\u0131 yakla\u015f\u0131mlar\u0131 ke\u015ffetmekten \u00e7ekinmemektir.<\/p>\n
Ger\u00e7ek D\u00fcnya Uygulamalar\u0131 ve \u0130leri Ad\u0131mlar<\/h2>\n
\u015eimdiye kadar basit bir s\u0131n\u0131fland\u0131rma problemi \u00fczerinde Scikit-learn ile model kurmay\u0131 \u00f6\u011frendik. Peki, bu \u00f6\u011frendiklerimizi ger\u00e7ek d\u00fcnyada nerede kullanabiliriz? Makine \u00f6\u011frenmesi, hayat\u0131m\u0131z\u0131n her alan\u0131na dokunuyor ve sundu\u011fu potansiyel inan\u0131lmaz derecede geni\u015f.<\/p>\n
E-ticarette<\/strong>, m\u00fc\u015fterilerin ge\u00e7mi\u015f sat\u0131n alma davran\u0131\u015flar\u0131na g\u00f6re onlara \u00f6zel \u00fcr\u00fcn \u00f6nerileri sunmak i\u00e7in makine \u00f6\u011frenmesi modelleri kullan\u0131l\u0131r. Bir kullan\u0131c\u0131n\u0131n sepete ekledi\u011fi \u00fcr\u00fcnlere g\u00f6re benzer \u00fcr\u00fcnleri \u00f6nermek veya ilgi alanlar\u0131na g\u00f6re kampanyalar olu\u015fturmak, sat\u0131\u015flar\u0131 art\u0131rman\u0131n etkili yollar\u0131d\u0131r. Finans sekt\u00f6r\u00fcnde<\/strong>, kredi kart\u0131 doland\u0131r\u0131c\u0131l\u0131\u011f\u0131n\u0131 tespit etmek, kredi riskini de\u011ferlendirmek veya borsa trendlerini tahmin etmek i\u00e7in karma\u015f\u0131k modeller geli\u015ftirilir. Sahtekarl\u0131k tespit sistemleri, anormallikleri tespit ederek b\u00fcy\u00fck finansal kay\u0131plar\u0131 \u00f6nleyebilir.<\/p>\n
Sa\u011fl\u0131k sekt\u00f6r\u00fcnde<\/strong>, t\u0131bbi g\u00f6r\u00fcnt\u00fcleri analiz ederek hastal\u0131klar\u0131 te\u015fhis etmek, hasta verilerini kullanarak hastal\u0131klar\u0131n ilerlemesini tahmin etmek veya ila\u00e7 ke\u015fif s\u00fcre\u00e7lerini h\u0131zland\u0131rmak i\u00e7in makine \u00f6\u011frenmesi kritik bir rol oynar. \u00d6rne\u011fin, r\u00f6ntgen g\u00f6r\u00fcnt\u00fclerindeki kanserli h\u00fccreleri tespit eden algoritmalar, doktorlara yard\u0131mc\u0131 olabilir.<\/p>\n
Sosyal medyada<\/strong>, kullan\u0131c\u0131lar\u0131n ilgi alanlar\u0131na g\u00f6re i\u00e7erik ak\u0131\u015flar\u0131n\u0131 ki\u015fiselle\u015ftirmek, istenmeyen yorumlar\u0131 (spam) filtrelemek veya duygu analizi yaparak kullan\u0131c\u0131lar\u0131n genel ruh halini anlamak i\u00e7in kullan\u0131l\u0131r. Bir markan\u0131n sosyal medyadaki alg\u0131s\u0131n\u0131 \u00f6l\u00e7mek veya bir \u00fcr\u00fcn hakk\u0131ndaki genel g\u00f6r\u00fc\u015fleri anlamak i\u00e7in duygu analizi b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r.<\/p>\n
Otonom ara\u00e7lar<\/strong>, \u00e7evrelerindeki nesneleri tan\u0131mak, yollar\u0131 belirlemek ve g\u00fcvenli bir s\u00fcr\u00fc\u015f sa\u011flamak i\u00e7in geli\u015fmi\u015f makine \u00f6\u011frenmesi modellerini kullan\u0131r. Trafik i\u015faretlerini tan\u0131mak, yayalar\u0131 alg\u0131lamak ve di\u011fer ara\u00e7larla etkile\u015fimde bulunmak bu modellerin temel g\u00f6revlerindendir.<\/p>\n
Bu sadece buzda\u011f\u0131n\u0131n g\u00f6r\u00fcnen k\u0131sm\u0131. Makine \u00f6\u011frenmesi, m\u00fc\u015fteri hizmetlerinde chatbot’lardan, spam filtrelerine, m\u00fczik \u00f6neri sistemlerinden do\u011fal dil i\u015flemeye kadar say\u0131s\u0131z alanda devrim yarat\u0131yor. Scikit-learn, bu uygulamalar\u0131n temelini olu\u015fturan algoritmalar\u0131 ve ara\u00e7lar\u0131 sa\u011flayarak bu alanda \u00e7al\u0131\u015fmak isteyenler i\u00e7in harika bir ba\u015flang\u0131\u00e7 noktas\u0131 sunuyor.<\/p>\n
\u0130leri ad\u0131mlar<\/strong> i\u00e7in neler yapabilirsiniz? \u0130lk olarak, daha karma\u015f\u0131k veri setleri \u00fczerinde \u00e7al\u0131\u015fmaya ba\u015flay\u0131n. Kaggle gibi platformlarda bulunan \u00e7e\u015fitli veri setleri, farkl\u0131 problemleri ke\u015ffetmeniz i\u00e7in harika kaynaklard\u0131r. \u0130kinci olarak, farkl\u0131 makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131 derinlemesine \u00f6\u011frenin. Sadece nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 bilmek de\u011fil, ayn\u0131 zamanda \u00e7al\u0131\u015fma prensiplerini anlamak da \u00f6nemlidir. \u00dc\u00e7\u00fcnc\u00fc olarak, derin \u00f6\u011frenme (deep learning) alan\u0131na g\u00f6z at\u0131n. TensorFlow ve PyTorch gibi k\u00fct\u00fcphaneler, daha karma\u015f\u0131k ve g\u00fc\u00e7l\u00fc modeller olu\u015fturman\u0131za olanak tan\u0131r. Son olarak, projelerinizi GitHub gibi platformlarda payla\u015farak toplulukla etkile\u015fim kurun ve geri bildirim al\u0131n. Makine \u00f6\u011frenmesi s\u00fcrekli geli\u015fen bir alan, bu nedenle \u00f6\u011frenmeye ve denemeye devam etmek en \u00f6nemli \u015feydir.<\/p>\n
Sonu\u00e7: Makine \u00d6\u011frenmesi Yolculu\u011funuz Ba\u015fl\u0131yor!<\/h2>\n
Bu kapsaml\u0131 rehberde, Scikit-learn k\u00fct\u00fcphanesini kullanarak Python ile basit bir makine \u00f6\u011frenmesi modeli kurma s\u00fcrecini ad\u0131m ad\u0131m inceledik. Temel kavramlardan ba\u015flayarak, gerekli ara\u00e7lar\u0131 kurduk, bir veri seti haz\u0131rlad\u0131k, ilk modelimizi e\u011fittik, performans\u0131n\u0131 de\u011ferlendirdik ve model iyile\u015ftirme tekniklerine g\u00f6z att\u0131k. G\u00f6rd\u00fc\u011f\u00fcn\u00fcz gibi, makine \u00f6\u011frenmesi d\u00fcnyas\u0131na ad\u0131m atmak, g\u00f6r\u00fcnd\u00fc\u011f\u00fc kadar karma\u015f\u0131k olmak zorunda de\u011fil. Scikit-learn’\u00fcn sundu\u011fu kullan\u0131c\u0131 dostu aray\u00fcz ve geni\u015f fonksiyonellik sayesinde, yaz\u0131l\u0131m geli\u015ftirme becerilerinizi makine \u00f6\u011frenmesi projelerine kolayca entegre edebilirsiniz.<\/p>\n
Unutmay\u0131n, her ba\u015far\u0131l\u0131 makine \u00f6\u011frenmesi projesinin temeli iyi bir problem tan\u0131m\u0131, temiz ve do\u011fru haz\u0131rlanm\u0131\u015f veriler ve do\u011fru algoritma se\u00e7imidir. Modelinizin performans\u0131n\u0131 de\u011ferlendirmek ve iyile\u015ftirmek i\u00e7in \u00e7e\u015fitli metrikleri kullanmay\u0131 \u00f6\u011frenmek de kritik \u00f6neme sahiptir. Bu rehber, size bu yolculukta bir ba\u015flang\u0131\u00e7 noktas\u0131 sunmaktad\u0131r. Kendi projelerinizi geli\u015ftirmeye, farkl\u0131 veri setleri \u00fczerinde denemeler yapmaya ve bu heyecan verici alanda kendinizi daha da geli\u015ftirmeye devam edin. Yapay zeka ve makine \u00f6\u011frenmesi gelece\u011fi \u015fekillendiriyor ve siz de bu gelece\u011fin bir par\u00e7as\u0131 olabilirsiniz!<\/p>\n
S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n
    \n
  • \n
    Makine \u00f6\u011frenmesi modelleri i\u00e7in hangi programlama dili en pop\u00fcler?<\/h4>\n
    Makine \u00f6\u011frenmesi alan\u0131nda en pop\u00fcler programlama dili \u015f\u00fcphesiz Python’dur. Bunun ba\u015fl\u0131ca nedenleri, zengin k\u00fct\u00fcphane ekosistemi (Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy gibi), okunabilirli\u011fi y\u00fcksek s\u00f6z dizimi ve geni\u015f bir topluluk deste\u011fine sahip olmas\u0131d\u0131r.<\/p>\n<\/li>\n
  • \n
    Scikit-learn’\u00fc kullanmak i\u00e7in derin matematik bilgisi gerekli mi?<\/h4>\n
    Scikit-learn, \u00e7o\u011fu makine \u00f6\u011frenmesi algoritmas\u0131n\u0131 soyutlayarak, kullan\u0131c\u0131lar\u0131n karma\u015f\u0131k matematiksel form\u00fcllerle do\u011frudan u\u011fra\u015fmadan modeller olu\u015fturmas\u0131na olanak tan\u0131r. Ancak, algoritmalar\u0131n nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 temel d\u00fczeyde anlamak, model se\u00e7imini ve performans\u0131n\u0131 yorumlamay\u0131 kolayla\u015ft\u0131r\u0131r. Ba\u015flang\u0131\u00e7 i\u00e7in ileri d\u00fczey matematik bilgisi \u015fart de\u011fildir, ancak \u00f6\u011frenme s\u00fcrecinde matematiksel temelleri g\u00fc\u00e7lendirmek faydal\u0131 olacakt\u0131r.<\/p>\n<\/li>\n
  • \n
    Bir makine \u00f6\u011frenmesi modeli olu\u015ftururken en s\u0131k kar\u015f\u0131la\u015f\u0131lan zorluklar nelerdir?<\/h4>\n
    En s\u0131k kar\u015f\u0131la\u015f\u0131lan zorluklar aras\u0131nda veri kalitesi sorunlar\u0131 (eksik de\u011ferler, ayk\u0131r\u0131 de\u011ferler, yanl\u0131\u015f etiketleme), a\u015f\u0131r\u0131 uyum (overfitting) veya eksik uyum (underfitting) sorunlar\u0131, do\u011fru hiperparametreleri bulma, modelin yorumlanabilirli\u011fi ve ger\u00e7ek d\u00fcnya verilerine genelleme yapma yetene\u011fi yer al\u0131r. Ayr\u0131ca, problemin do\u011fru bir \u015fekilde tan\u0131mlanmas\u0131 ve uygun metriklerin se\u00e7ilmesi de \u00f6nemlidir.<\/p>\n<\/li>\n
  • \n
    Scikit-learn ile hangi t\u00fcr makine \u00f6\u011frenmesi problemlerini \u00e7\u00f6zebilirim?<\/h4>\n
    Scikit-learn, s\u0131n\u0131fland\u0131rma (classification), regresyon (regression), k\u00fcmeleme (clustering), boyut indirgeme (dimensionality reduction), model se\u00e7imi ve \u00f6n i\u015fleme gibi \u00e7ok \u00e7e\u015fitli makine \u00f6\u011frenmesi g\u00f6revleri i\u00e7in ara\u00e7lar sunar. Bu, metin s\u0131n\u0131fland\u0131rmadan g\u00f6r\u00fcnt\u00fc tan\u0131maya, m\u00fc\u015fteri segmentasyonundan anomali tespitine kadar bir\u00e7ok farkl\u0131 problemi \u00e7\u00f6zebilece\u011finiz anlam\u0131na gelir.<\/p>\n<\/li>\n
  • \n
    Makine \u00f6\u011frenmesi projelerimde hangi veri setlerini kullanabilirim?<\/h4>\n
    Scikit-learn’\u00fcn kendi i\u00e7inde bar\u0131nd\u0131rd\u0131\u011f\u0131 \u00f6rnek veri setleri (Iris, Digits, Boston Housing vb.) ba\u015flang\u0131\u00e7 i\u00e7in harikad\u0131r. Bunun yan\u0131 s\u0131ra, Kaggle, UCI Machine Learning Repository, Google Dataset Search gibi platformlarda binlerce farkl\u0131 veri seti bulunmaktad\u0131r. Kendi toplad\u0131\u011f\u0131n\u0131z veya olu\u015fturdu\u011funuz verileri de kullanabilirsiniz.<\/p>\n<\/li>\n<\/ul>\n<\/article>\n","protected":false},"excerpt":{"rendered":"Yapay zeka ve makine \u00f6\u011frenmesi r\u00fczgar\u0131 esiyor, de\u011fil mi? Her g\u00fcn yeni bir makale, yeni bir ara\u00e7, yeni…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1513,1],"tags":[],"class_list":{"0":"post-44730","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-dusunsel-ve-kisisel","7":"category-genel","8":"cs-entry","9":"cs-video-wrap"},"yoast_head":"\nMakine \u00d6\u011frenmesi D\u00fcnyas\u0131na Scikit-learn ile H\u0131zl\u0131 Bir Giri\u015f Yapmak M\u00fcmk\u00fcn M\u00fc?<\/title>\n<meta name=\"description\" content=\"Yapay zeka ve makine \u00f6\u011frenmesi r\u00fczgar\u0131 esiyor, de\u011fil mi? Her g\u00fcn yeni bir makale, yeni bir ara\u00e7, yeni bir ba\u015far\u0131 hikayesiyle kar\u015f\u0131la\u015f\u0131yoruz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-dunyasina-scikit-learn-ile-hizli-bir-giris-yapmak-mumkun-mu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenmesi D\u00fcnyas\u0131na Scikit-learn ile H\u0131zl\u0131 Bir Giri\u015f Yapmak M\u00fcmk\u00fcn M\u00fc?\" \/>\n<meta property=\"og:description\" content=\"Yapay zeka ve makine \u00f6\u011frenmesi r\u00fczgar\u0131 esiyor, de\u011fil mi? 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