k-eng yaqin qo'shni (knn) algoritmi
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![0 7712 df.corrwith(df['diagnosis']).abs().sort_values(ascending=false) diagnosis 1.000000 concave points_worst 0.793566 perimeter_worst 0.782914 concave points_mean 0.776614 radius_worst 0.776454 сохранение… 01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 7/11 perimeter_mean 0.742636 area_worst 0.733825 radius_mean 0.730029 area_mean 0.708984 concavity_mean 0.696360 concavity_worst 0.659610 compactness_mean 0.596534 compactness_worst 0.590998 radius_se 0.567134 perimeter_se 0.556141 area_se 0.548236 texture_worst 0.456903 smoothness_worst 0.421465 symmetry_worst 0.416294 texture_mean 0.415185 concave points_se 0.408042 smoothness_mean 0.358560 symmetry_mean 0.330499 fractal_dimension_worst 0.323872 compactness_se 0.292999 concavity_se 0.253730 fractal_dimension_se 0.077972 smoothness_se 0.067016 id 0.039769 fractal_dimension_mean 0.012838 texture_se 0.008303 symmetry_se 0.006522 dtype: float64 ml ga tayyorlaymiz x = df.drop('diagnosis', axis=1).values y = df['diagnosis'] from sklearn.preprocessing import standardscaler scaler = standardscaler() x = scaler.fit_transform(x) ▾ kneighborsclassifier kneighborsclassifier() # train/test split from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=12) # k-nn from sklearn.neighbors import kneighborsclassifier knn = kneighborsclassifier(n_neighbors=5) # k-ni qiymati knn.fit(x_train, y_train) сохранение… 01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 8/11 y_pridect=knn.predict(x_test) print(y_test.to_numpy()) …](/media/previews/pages/preview_NSNvjsk.webp "k-eng yaqin qo'shni (knn) algoritmi - Page 4")
![0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0] [1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0] baholash jaccard index from sklearn.metrics import jaccard_score jaccard_score(y_test, y_pridect) 0.8775510204081632 confusion matrix from sklearn.metrics import confusion_matrix import seaborn as …](/media/previews/pages/preview_ZZ3GdGB.webp "k-eng yaqin qo'shni (knn) algoritmi - Page 5")
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01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 1/11 baxolash deganda biz nimani tushunamiz bizda ma'lum dataset bor unda hajm, rang va class ma'lumotlari mavjud. biz modelni yaratgandan kiyin huddi shu qiymatlarni bashorat qilib ko'ramiz, va biz asil qiymatlar va bashorat qiymatlarga ega bo'lamiz. сохранение… 01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 2/11 bu qiymatlarni solishtirish uchun qanday yul tutamiz. klassi�katsiyada bir nechta baxolash mezonlari mavjud. 1. jaccard index - ikki to'plam o'rtasidagi o'xshashlikni ko'rsatadi, va uning formulasi qo'yd...
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