3 - Scikit-Learn - Giris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Giriş"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scikit-Learn**, Python için en çok kullanılan ve en kapsamlı yapay öğrenme kütüphanelerinden birisidir. Numpy, Matplotlib, Pandas gibi kütüphaneler ile entegre kullanılabilir. Bunun yanında derin öğrenme gibi eksik olduğu noktalarda diğer yapay öğrenme kütüphaneleri ile de iyi anlaşır. Genel olarak kullanımı kolaydır. Detaylar için: https://scikit-learn.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scikit-Learn içindeki Hazır Veri Setleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn içinde bir takım hazır veri seti vardır. Bu siteden detaylarına ulaşabilirsiniz: https://scikit-learn.org/stable/datasets/index.html. Bu veri setlerine ulaşmak için hazır işlevler mevcuttur. Veri setini, numpy arrayleri içeren, `Bunch` isimli bir veri yapısında yükler. Girdi ve hedef verilerine, köşeli parantez ile ulaşmak mümkündür."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "iris_data = datasets.load_iris()\n",
    "print('Scikit: ',type(iris_data))\n",
    "print(dir(iris_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DESCR veri setini anlatan bilgiler içerir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(iris_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Girdi: ', type(iris_data['data']))\n",
    "print('Hedef: ', type(iris_data['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(iris_data['data'].shape, iris_data['target'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(iris_data['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(iris_data['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(iris_data['feature_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimator API: Tahminleyici Uygulama Programı Arayüzü\n",
    "\n",
    "Scikit-Learn kütüphanesindeki bütün yapay öğrenme algoritmaları Estimator API kullanılarak yazılmıştır. Bu API sayesinde algoritmaları tutarlı bir şekilde kullanmak mümkündür. Bu APInin temel özellellikleri anlaşıldıktan sonra, Scikit-Learn kütüphanesini kullanmak çok kolaydır. \n",
    "\n",
    "Bu APIyi kullanan bir algoritmayı kullanmak için genel olarak aşağıdaki adımlar izlenir:\n",
    "\n",
    "1. Problemimize uygun yapay öğrenme modelini gerçekleştirecek `estimator` sınıfını `import` etmek\n",
    "2. Modelin hiperparametrelerini belirlemek. (Daha sonra bu parametreleri nasıl seçeceğimizi göreceğiz)\n",
    "3. Verilerimizi hazırlamak (Gözetimli öğrenme için girdi ve hedef verilerini, gözetimsiz öğrenme için doğrudan veriyi)\n",
    "4. Modeli `fit()` fonksiyonunu çağırarak ile öğrenmek.\n",
    "5. Yeni veri üzerinde çıkarım yapmak:\n",
    "  * Gözetimli öğrenme için `predict()` fonksiyonunu çağırarak\n",
    "  * Gözetimsiz öğrenme için, modele göre, `transform()` ya da `predict()` fonksiyonlarından birisini çağırarak.\n",
    "\n",
    "Bu adımları bir doğrusal regresyon problemi üzerinden takip edelim. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = np.linspace(-1,1,50)\n",
    "y = (2*x + 1) + np.random.normal(0,0.25,len(x))\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Uygun modeli yüklemek. Bu örnek için \"linear regression\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Hiperparametrelerimizi belirleyelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nelere ihtiyacımız olduğunu görmek için:\n",
    "from inspect import signature\n",
    "print(signature(LinearRegression))\n",
    "# Dökümantasyon için:\n",
    "# print(help(LinearRegression))\n",
    "model = LinearRegression(fit_intercept = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Verimizi hazırlayalım. Scikit-Learn, girdileri 2 boyutlu, hedefleri de 1 boyutlu olarak bekliyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x.shape) #bir boyutlu\n",
    "X = x[:,np.newaxis] #iki boyutlu yaptık, alternatif olarak X = x.reshape(len(x),1)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Öğrenelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X,y)\n",
    "print('Öğrenilmiş Ağırlık: ', model.coef_)\n",
    "print('Öğrenilmiş Kesim (intercept): ', model.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Yeni veri gelince"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#-1 ve 1 arasında rastgele veri yaratalım\n",
    "xnew = -1 + 2*np.random.rand(10)\n",
    "ynew = model.predict(xnew[:,np.newaxis])\n",
    "plt.scatter(xnew, ynew)\n",
    "#Gercek model ile denemek istersek\n",
    "ytrue = 2*xnew + 1\n",
    "plt.scatter(xnew,ytrue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geçtiğimiz örnek için orjinal verimiz ile modelin yakınlığına da bakabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ypred = model.predict(X)\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,ypred,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grafik beklediğimiz üzere kötü durmuyor. Modelin sayısal performansına (örneğin: ortalama karesel hatasına) da bakabiliriz. Scikit-learn, `metrics` modulü ile bu konuda işlevsellik sağlıyor. (Değişik model performansları ve daha fazlası için: https://scikit-learn.org/stable/modules/model_evaluation.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y, ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buraya kadar modelimizin eğitim kümesi üzerindeki performansına baktık. Ancak biliyoruz ki daha kapsamlı performans testleri gerekiyor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performans Testleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yukarıda gördüğümüz adımlar, sürecin önemli iki kısmını atlıyor. Bunlardan birincisi, öğrenilen modelin performans testi, ikincisi ise doğru hiperparametre seçilmesi. \n",
    "\n",
    "Bunun için iris veri setimize geri dönelim ve algoritma olarak da Logistic Regression ile başlayalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "#print(signature(LogisticRegression))\n",
    "#Şimdilik varsayılan hiperparametre değerleri ile devam edelim.\n",
    "#Sadece iterasyon sayısını arttırıyoruz ve uyarıları bastırmak için iki parametre veriyoruz\n",
    "#Sorun varsa multi_class = 'ovr'\n",
    "logregModel = LogisticRegression(multi_class = 'auto', solver ='lbfgs', max_iter = 1000) \n",
    "X = iris_data['data']\n",
    "y = iris_data['target']\n",
    "logregModel.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelin öğrenme performansına bakalım. (Uyarı: Veriyi henüz eğitim kümesi, validasyon kümesi ve test kümesi diye ayırmadık)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "ypred = logregModel.predict(X)\n",
    "accuracy_score(y, ypred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bütün veri seti ile öğrenme performansı iyi ancak modeli görmediğimiz veri üzerinde denememiz gerekli. Scikit-learn, veriyi ayırmak için işlevsellik sağlıyor. `train_test_split` sınıfına `test_size` parametresi vererek ayırma oranlarını belirleniyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_selection çalışmaz ise cross_validation\n",
    "from sklearn.model_selection import train_test_split\n",
    "#Hep aynı şekilde ayırması için random seed belirliyoruz. \n",
    "#Bu şart değil ama geliştirme aşamasında faydalı\n",
    "#80%-20% ayırırsak \n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.2, \n",
    "                                                random_state = 12)\n",
    "logregModel.fit(Xtrain,ytrain)\n",
    "ypred = logregModel.predict(Xtest)\n",
    "accuracy_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bildiğimiz üzere, tek bir test her zaman en sağlıklı yaklaşım değil. Örneğin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#Hep aynı şekilde ayırması için random seed belirliyoruz. Bu şart değil ama geliştirme aşamasında faydalı\n",
    "#80%-20% ayırırsak \n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.2, \n",
    "                                                random_state = 12321)\n",
    "logregModel.fit(Xtrain,ytrain)\n",
    "ypred = logregModel.predict(Xtest)\n",
    "accuracy_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu yüzden çapraz geçerlilik (cross-validation) yaklaşımını kullanıyoruz. Scikit-learn kütüphanesindeki `cross_val_score` sınıfı bu işlevselliği sağlıyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "crossValScores = cross_val_score(logregModel, X, y, cv=5)\n",
    "print('Değerler: ', crossValScores)\n",
    "print('Ortalama (2 standart sapma): {0:.2f} (+/- {0:.2f})'.format(\n",
    "      crossValScores.mean(), crossValScores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Çapraz geçerlilik sırasında rastgele bölmek önemli. Bunun için `ShuffleSplit` ve `KFold` sınıflarını kullanıyoruz. ShuffleSplit, veriyi, belirlenen **orana** göre verilen sayı kadar rastgele ayırıyor. KFold, (shuffle=True iken), veriyi karıştırdıktan sonra verilen sayı kadar parçaya bölüyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit, KFold\n",
    "\n",
    "#Rastgele 5 parçaya bölmek için:\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.25)\n",
    "print('Her seferinde rastgele bölünce: ')\n",
    "print(cross_val_score(logregModel, X, y, cv=cv))\n",
    "\n",
    "#Rastgele karıştırıp düzenli 5 parçaya bölmek için\n",
    "cv = KFold(n_splits=5, shuffle=True)\n",
    "print('Bir kere rastgele karıştırıp düzenli bölünce:')\n",
    "print(cross_val_score(logregModel, X, y, cv=cv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu örnek için olmasa da birçok veri setinde, sınıfların dağılım oranı farklı olabilir. Performans ölçerken buna dikkat etmemiz gerekli. Bunun için, yukarıdaki sınıfların *Stratified* versiyonları da var."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold\n",
    "\n",
    "cv = StratifiedShuffleSplit(n_splits=5, test_size=0.25)\n",
    "print('Sınıf dağılımına göre dağıtınca:')\n",
    "print(cross_val_score(logregModel, X, y, cv=cv))\n",
    "\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "print('Sınıf dağılımına göre dağıtınca:')\n",
    "print(cross_val_score(logregModel, X, y, cv=cv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Hiperparametre Seçimi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buraya kadar performans testi yaptık. Ancak bu testler sabit hiperparametre seçimleri için geçerliydi. Scikit-learn bunu yapmak için iki temel işlevsellik sunuyor:\n",
    "1. Grid Taraması: Verilen hiperparametre değerlerinin bütün kombinasyonlarının denenmesi ve verilen performans testine göre en iyi kombinasyonun bulunması. `GridSearchCV`\n",
    "2. Rastgele Tarama: Verilen hiperparametre dağılımlarından belirlenen sayıda örneklem alınarak denenmesi ve verilen performans testine göre en iyi kombinasyonun bulunması. `RandomizedSearchCV`\n",
    "\n",
    "İlk olarak Grid Taraması ile başlayalım. Logistic regression algoritmasının en temel hiperparametresi, regularizasyon ağırlığı. Bunu `C` ile belirliyoruz. Bu parametre, regülarizasyon ağırlığı ile ters orantılı (düşük C ==> yüksek regülarizasyon). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#Belirlediğimiz performans testi\n",
    "cv=StratifiedKFold(n_splits=5, shuffle=True)\n",
    "paramsToTest = {'C':[0.1,0.5,1.0,2.5,5.0]}\n",
    "gscv = GridSearchCV(LogisticRegression(multi_class = 'auto', solver ='lbfgs', \n",
    "                                       max_iter = 1000), \n",
    "                    paramsToTest, cv=cv,return_train_score=True)\n",
    "gscv.fit(X, y)\n",
    "print('En iyi C: ',  gscv.best_estimator_.C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En iyi hiperparametre setini bulduktan sonra bütün veri ve bu parametre seti ile tekrar modeli öğreniyor. Bu `refit` değişkeni ile ayarlanıyor (varsayılan True). Hiperparametre seçim işleminin istatistiklerine `gscv.cv_results_` yapısı üzerinden erişmek mümkün. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(gscv.cv_results_.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sadece bir kısmına bakacak olursak:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(gscv.cv_results_['params'])\n",
    "print(gscv.cv_results_['mean_test_score'])\n",
    "print(gscv.cv_results_['std_test_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu değerleri kullanarak validasyon eğrileri çizdirmek ve görsel olarak sonuçları sunmak mümkün. Bunun yanında scikit-learn `validation_curve` adı altında benzer bir işlevsellik sunuyor. Bu fonksiyona regresyonda tekrar bakacağız."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rastgele tarama için liste olarak değerlere ek olarak değerler üzerinden bir dağılım da belirtilebilir. Liste kullanılırsa, bu listeden rastgele örneklem alınıyor. Eğer bir dağılım belirtilmişse, buna göre örneklem alınıyor. Bunun için dağılımların `rvs` metodu ile örneklem sağlamaları gerekli. `scipy.stats` modülündeki dağılımlar bunu sağlıyor ancak kendimiz de gerekirse yazabiliriz. Ek olarak, kaç adet örneklem alınacağının `n_iter` ile belirtilmesi lazım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform as sp_uniform \n",
    "\n",
    "#Belirlediğimiz performans testi\n",
    "cv=StratifiedKFold(n_splits=5, shuffle=True)\n",
    "paramsToTest = {'C':np.arange(0.1,5.1,0.1)}\n",
    "rscv = RandomizedSearchCV(LogisticRegression(multi_class = 'auto', \n",
    "                                             solver ='lbfgs', max_iter = 1000), \n",
    "                          paramsToTest, cv=cv, n_iter=10)\n",
    "rscv.fit(X,y)\n",
    "print('En iyi C: ',  rscv.best_estimator_.C)\n",
    "\n",
    "paramsToTest = {'C': sp_uniform(0.1,4.9) }\n",
    "rscv = RandomizedSearchCV(LogisticRegression(multi_class = 'auto', \n",
    "                                             solver ='lbfgs', max_iter = 1000), \n",
    "                          paramsToTest, cv=cv, n_iter=10)\n",
    "rscv.fit(X,y)\n",
    "print('En iyi C: ',  rscv.best_estimator_.C)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Değerlendirmesi\n",
    "\n",
    "Performans testlerini ve hiperparametre seçimlerini gördük. Şimdi baştan sona birleştirelim. Bunu anlatmak için scikit-learn kaynaklarından bir resim kullanırsak:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "İyice emin olmak için, bu adım da rastgele birden fazla kere çalıştırılmalıdır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression Modelini değerlendirmek için:\n",
    "* Bütün veriyi 60%-40% eğitim-test kümesi olarak ayırmak\n",
    "* Eğitim kümesinden öğrenilenleri, 3'lü çapraz geçerlilik kullanarak ölçmek\n",
    "* Hiperparametre bulmak için 0.1 ve 5 arasında rastgele 10 değer seçmek.\n",
    "* En sonunda seçilen hiperparametre ile bütün eğitim kümesinini kullanarak öğrenmek\n",
    "* Test kümesini kullanarak son modelin performansını değerlendirmek\n",
    "\n",
    "(Uyarı: Bu adımlar kompleks modeller ve/veya çok büyük veri setleri için zaman sorunlarından ötürü uygulanamayabilirler.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Sıfırdan yazacak olursak\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV\n",
    "from scipy.stats import uniform as sp_uniform\n",
    "\n",
    "#Veriyi yükle\n",
    "iris_data = datasets.load_iris()\n",
    "y = iris_data['target']\n",
    "X = iris_data['data']\n",
    "\n",
    "#Veriyi ayır. Burada ayırırken sınıf oranlarını koruyoruz (stratify=y)\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.4, \n",
    "                                                stratify=y)\n",
    "\n",
    "#Çapraz geçerlilik yaklaşımını ayarla\n",
    "cv=StratifiedKFold(n_splits=3, shuffle=True)\n",
    "\n",
    "#Eğitim kümesi üzerinden hiperparametre taramasını ayarla\n",
    "paramsToTest = {'C': sp_uniform(0.1,4.9)}\n",
    "rscv = RandomizedSearchCV(LogisticRegression(multi_class = 'auto', solver ='lbfgs', max_iter = 1000),\n",
    "                          paramsToTest, cv=cv, n_iter=10, iid=True)\n",
    "\n",
    "#Taramayı başlat ve seç\n",
    "rscv.fit(Xtrain,ytrain)\n",
    "print('Seçilen C: ', rscv.best_estimator_.C)\n",
    "\n",
    "#Bu noktada rscv.best_estimator_ bütün eğitim kümesi ile öğrenilmiş modeli barındırıyor. \n",
    "#Ancak rscv üzerinden doğrudan predict fonksiyonunu çağırmak mümkün\n",
    "# Performans test etmek için:\n",
    "ypred = rscv.predict(Xtest)\n",
    "print(accuracy_score(ypred,ytest))\n",
    "\n",
    "# Hata matrisine de bakacak olursak\n",
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(ypred, ytest)\n",
    "print(mat)\n",
    "plt.matshow(mat)\n",
    "plt.xlabel('gerçek sınıf')\n",
    "plt.ylabel('tahminlenen sınıf');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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