{"id":1165,"date":"2023-07-27T10:43:39","date_gmt":"2023-07-27T10:43:39","guid":{"rendered":"https:\/\/statorials.org\/tr\/pythonda-dogrusal-diskriminant-analizi\/"},"modified":"2023-07-27T10:43:39","modified_gmt":"2023-07-27T10:43:39","slug":"pythonda-dogrusal-diskriminant-analizi","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pythonda-dogrusal-diskriminant-analizi\/","title":{"rendered":"Python&#39;da do\u011frusal diskriminant analizi (ad\u0131m ad\u0131m)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/tr\/dogrusal-diskriminant-analizi\/\" target=\"_blank\" rel=\"noopener noreferrer\">Do\u011frusal diskriminant analizi,<\/a> bir dizi \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkeniniz oldu\u011funda ve bir <a href=\"https:\/\/statorials.org\/tr\/degiskenleri-aciklayici-yanitlar\/\" target=\"_blank\" rel=\"noopener noreferrer\">yan\u0131t de\u011fi\u015fkenini<\/a> iki veya daha fazla s\u0131n\u0131fa s\u0131n\u0131fland\u0131rmak istedi\u011finizde kullanabilece\u011finiz bir y\u00f6ntemdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu e\u011fitimde Python&#8217;da do\u011frusal diskriminant analizinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fine ili\u015fkin ad\u0131m ad\u0131m bir \u00f6rnek sunulmaktad\u0131r.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 1: Gerekli Kitapl\u0131klar\u0131 Y\u00fckleyin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u00d6ncelikle bu \u00f6rnek i\u00e7in gereken fonksiyonlar\u0131 ve k\u00fct\u00fcphaneleri y\u00fckleyece\u011fiz:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedStratifiedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> cross_val_score\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">discriminant_analysis<\/span> <span style=\"color: #008000;\">import<\/span> LinearDiscriminantAnalysis \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/b><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>2. Ad\u0131m: Verileri y\u00fckleyin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Bu \u00f6rnek i\u00e7in sklearn k\u00fct\u00fcphanesindeki <strong>iris<\/strong> veri setini kullanaca\u011f\u0131z. A\u015fa\u011f\u0131daki kod, bu veri k\u00fcmesinin nas\u0131l y\u00fcklenece\u011fini ve kullan\u0131m kolayl\u0131\u011f\u0131 i\u00e7in onu bir pandas DataFrame&#8217;e nas\u0131l d\u00f6n\u00fc\u015ft\u00fcrece\u011finizi g\u00f6sterir:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>iris<\/em> dataset<\/span>\niris = datasets. <span style=\"color: #3366ff;\">load_iris<\/span> ()\n\n<span style=\"color: #008080;\">#convert dataset to pandas DataFrame\n<\/span>df = pd.DataFrame(data = np.c_[iris[' <span style=\"color: #008000;\">data<\/span> '], iris[' <span style=\"color: #008000;\">target<\/span> ']],\n                 columns = iris[' <span style=\"color: #008000;\">feature_names<\/span> '] + [' <span style=\"color: #008000;\">target<\/span> '])\ndf[' <span style=\"color: #008000;\">species<\/span> '] = pd. <span style=\"color: #3366ff;\">Categorical<\/span> . <span style=\"color: #3366ff;\">from_codes<\/span> (iris.target, iris.target_names)\ndf.columns = [' <span style=\"color: #008000;\">s_length<\/span> ', ' <span style=\"color: #008000;\">s_width<\/span> ', ' <span style=\"color: #008000;\">p_length<\/span> ', ' <span style=\"color: #008000;\">p_width<\/span> ', ' <span style=\"color: #008000;\">target<\/span> ', ' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#view first six rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n   s_length s_width p_length p_width target species\n0 5.1 3.5 1.4 0.2 0.0 setosa\n1 4.9 3.0 1.4 0.2 0.0 setosa\n2 4.7 3.2 1.3 0.2 0.0 setosa\n3 4.6 3.1 1.5 0.2 0.0 setosa\n4 5.0 3.6 1.4 0.2 0.0 setosa\n\n<span style=\"color: #3366ff;\"><span style=\"color: #008080;\">#find how many total observations are in dataset<\/span>\n<span style=\"color: #000000;\">len( <span style=\"color: #3366ff;\">df.index<\/span> )\n\n150<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Veri setinin toplamda 150 g\u00f6zlem i\u00e7erdi\u011fini g\u00f6rebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu \u00f6rnekte, belirli bir \u00e7i\u00e7e\u011fin hangi t\u00fcre ait oldu\u011funu s\u0131n\u0131fland\u0131rmak i\u00e7in do\u011frusal bir diskriminant analiz modeli olu\u015fturaca\u011f\u0131z.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Modelde a\u015fa\u011f\u0131daki tahmin de\u011fi\u015fkenlerini kullanaca\u011f\u0131z:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Sepal uzunlu\u011fu<\/span><\/li>\n<li> <span style=\"color: #000000;\">Sepal geni\u015fli\u011fi<\/span><\/li>\n<li> <span style=\"color: #000000;\">Petal uzunlu\u011fu<\/span><\/li>\n<li> <span style=\"color: #000000;\">Yaprak geni\u015fli\u011fi<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Bunlar\u0131, a\u015fa\u011f\u0131daki \u00fc\u00e7 potansiyel s\u0131n\u0131f\u0131 destekleyen <em>T\u00fcr<\/em> yan\u0131t de\u011fi\u015fkenini tahmin etmek i\u00e7in kullanaca\u011f\u0131z:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">setosa<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u00e7ok renkli<\/span><\/li>\n<li> <span style=\"color: #000000;\">Virjinya<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>3. Ad\u0131m: LDA modelini ayarlay\u0131n<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Daha sonra, sklearn&#8217;in <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\" target=\"_blank\" rel=\"noopener noreferrer\">LinearDiscriminantAnalsys<\/a> fonksiyonunu kullanarak LDA modelini verilerimize uyarlayaca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = df[[' <span style=\"color: #008000;\">s_length<\/span> ',' <span style=\"color: #008000;\">s_width<\/span> ',' <span style=\"color: #008000;\">p_length<\/span> ',' <span style=\"color: #008000;\">p_width<\/span> ']]\ny = df[' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#Fit the LDA model\n<\/span>model = LinearDiscriminantAnalysis()\nmodel. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 4: Tahminlerde bulunmak i\u00e7in modeli kullan\u0131n<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Verilerimizi kullanarak modeli yerle\u015ftirdikten sonra, tekrarlanan katmanl\u0131 k-katl\u0131 \u00e7apraz do\u011frulamay\u0131 kullanarak modelin performans\u0131n\u0131 de\u011ferlendirebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu \u00f6rnek i\u00e7in 10 katlama ve 3 tekrar kullanaca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#Define method to evaluate model\n<span style=\"color: #000000;\">cv = RepeatedStratifiedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n<\/span>\n#evaluate model\n<span style=\"color: #000000;\">scores = cross_val_score(model, X, y, scoring=' <span style=\"color: #008000;\">accuracy<\/span> ', cv=cv, n_jobs=-1)\nprint( <span style=\"color: #3366ff;\">np.mean<\/span> (scores))<\/span>  \n\n<span style=\"color: #000000;\">0.9777777777777779<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Modelin ortalama <strong>%97,78<\/strong> do\u011fruluk oran\u0131na ula\u015ft\u0131\u011f\u0131n\u0131 g\u00f6rebiliyoruz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Modeli, giri\u015f de\u011ferlerine dayanarak yeni bir \u00e7i\u00e7e\u011fin hangi s\u0131n\u0131fa ait oldu\u011funu tahmin etmek i\u00e7in de kullanabiliriz:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new observation\n<\/span>new = [5, 3, 1, .4]\n\n<span style=\"color: #008080;\">#predict which class the new observation belongs to\n<\/span>model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n\narray(['setosa'], dtype='&lt;U10')\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Modelin bu yeni g\u00f6zlemin <em>setosa<\/em> ad\u0131 verilen t\u00fcre ait oldu\u011funu \u00f6ng\u00f6rd\u00fc\u011f\u00fcn\u00fc g\u00f6r\u00fcyoruz.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>5. Ad\u0131m: Sonu\u00e7lar\u0131 g\u00f6rselle\u015ftirin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Son olarak, modelin do\u011frusal ay\u0131r\u0131c\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirmek ve veri setimizdeki \u00fc\u00e7 farkl\u0131 t\u00fcr\u00fc ne kadar iyi ay\u0131rd\u0131\u011f\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in bir LDA grafi\u011fi olu\u015fturabiliriz:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data to plot\n<\/span>X = iris.data\ny = iris.target\nmodel = LinearDiscriminantAnalysis()\ndata_plot = model. <span style=\"color: #3366ff;\">fit<\/span> (x,y). <span style=\"color: #3366ff;\">transform<\/span> (X)\ntarget_names = iris. <span style=\"color: #3366ff;\">target_names<\/span>\n\n<span style=\"color: #008080;\">#create LDA plot\n<\/span>plt. <span style=\"color: #3366ff;\">figure<\/span> ()\ncolors = [' <span style=\"color: #008000;\">red<\/span> ', ' <span style=\"color: #008000;\">green<\/span> ', ' <span style=\"color: #008000;\">blue<\/span> ']\nlw = 2\n<span style=\"color: #008000;\">for<\/span> color, i, target_name <span style=\"color: #008000;\">in<\/span> zip(colors, [0, 1, 2], target_names):\n    plt. <span style=\"color: #3366ff;\">scatter<\/span> (data_plot[y == i, 0], data_plot[y == i, 1], alpha=.8, color=color,\n                label=target_name)\n\n<span style=\"color: #008080;\">#add legend to plot\n<\/span>plt. <span style=\"color: #3366ff;\">legend<\/span> (loc=' <span style=\"color: #008000;\">best<\/span> ', shadow= <span style=\"color: #008000;\">False<\/span> , scatterpoints=1)\n\n<span style=\"color: #008080;\">#display LDA plot\n<\/span>plt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11651 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ldapython1.png\" alt=\"Python'da do\u011frusal diskriminant analizi\" width=\"416\" height=\"281\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Bu e\u011fitimde kullan\u0131lan Python kodunun tamam\u0131n\u0131 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/linear_discriminant_analysis\" target=\"_blank\" rel=\"noopener noreferrer\">burada<\/a> bulabilirsiniz.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Do\u011frusal diskriminant analizi, bir dizi \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkeniniz oldu\u011funda ve bir yan\u0131t de\u011fi\u015fkenini iki veya daha fazla s\u0131n\u0131fa s\u0131n\u0131fland\u0131rmak istedi\u011finizde kullanabilece\u011finiz bir y\u00f6ntemdir. Bu e\u011fitimde Python&#8217;da do\u011frusal diskriminant analizinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fine ili\u015fkin ad\u0131m ad\u0131m bir \u00f6rnek sunulmaktad\u0131r. Ad\u0131m 1: Gerekli Kitapl\u0131klar\u0131 Y\u00fckleyin \u00d6ncelikle bu \u00f6rnek i\u00e7in gereken fonksiyonlar\u0131 ve k\u00fct\u00fcphaneleri y\u00fckleyece\u011fiz: from sklearn. model_selection import train_test_split [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1165","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python&#039;da Do\u011frusal Diskriminant Analizi (Ad\u0131m Ad\u0131m)<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde, ad\u0131m ad\u0131m bir \u00f6rnek de dahil olmak \u00fczere Python&#039;da do\u011frusal diskriminant analizinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi 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