k-eng yaqin qo'shni (knn) algoritmi

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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'ydagicha. a-asil qiymatlar b-bashorat qiymatlar ikkala to'plamni qo'ydagicha ko'rinishda yozib olamiz. bu yerda ko'k rangdagilar mos tushgan qiymatlar qizildagilar esa mos tushmagan qiymatlar. ❗jaccard indexi qancha katta bo'lsa, model aniqligi shuncha baland hisoblanadi 2.confusion matrix baxolash mezoni. bu quydagicha matritsa ko'rinishda bo'ladi. 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 3/11 bu matritsani quydagicha 0 va 1 ko'rinishidagi matritsaga o'girib olamiz чтобы изменить содержимое …

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anligi. classi�cation-klassi�katsiya k-eng yaqin qo'shni (knn) algoritmi ko'krak saratonini aniqlash tavsif: ko'krak saratoni dunyodagi ayollar orasida eng keng tarqalgan saraton hisoblanadi. bu barcha saraton holatlarining 25 foizini tashkil qiladi. ko'krark saratoni ko'krakdagi hujayralar nazoratsiz o'sishidan boshlanadi. ushbu hujayralar odatda rentgen nurlari orqali ko'rish mumkin bo'lgan o'simtalarni tahlil qilish orqali aniqlanadi. import pandas as pd import numpy as np url="diagnostik.csv" df = pd.read_csv(url) df.sample(10) 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 5/11 id diagnosis radius_mean texture_mean perimeter_mean area_mean smoot 70 859575 m 18.94 21.31 123.60 1130.0 476 911654 b 14.20 20.53 92.41 618.4 289 89143601 b 11.37 18.89 72.17 396.0 385 90291 m 14.60 23.29 93.97 664.7 1 842517 m 20.57 17.77 132.90 1326.0 487 913505 m 19.44 18.82 128.10 1167.0 225 88143502 b 14.34 13.47 92.51 641.2 287 8913 b 12.89 13.12 81.89 515.9 183 873843 b 11.41 14.92 73.53 402.0 331 896864 b 12.98 19.35 …

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$bs() corr_matrix.style.background_gradient(cmap='coolwarm') сохранение… 01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 6/11 id diagnosis radius_mean texture_mean perimeter_m id 1.000000 0.039769 0.074626 0.099770 0.073 diagnosis 0.039769 1.000000 0.730029 0.415185 0.7426 radius_mean 0.074626 0.730029 1.000000 0.323782 0.9978 texture_mean 0.099770 0.415185 0.323782 1.000000 0.3295 perimeter_mean 0.073159 0.742636 0.997855 0.329533 1.0000 area_mean 0.096893 0.708984 0.987357 0.321086 0.9865 smoothness_mean 0.012968 0.358560 0.170581 0.023389 0.2072 compactness_mean 0.000096 0.596534 0.506124 0.236702 0.5569 concavity_mean 0.050080 0.696360 0.676764 0.302418 0.716 concave points_mean 0.044158 0.776614 0.822529 0.293464 0.8509 symmetry_mean 0.022114 0.330499 0.147741 0.071401 0.1830 fractal_dimension_mean 0.052511 0.012838 0.311631 0.076437 0.2614 radius_se 0.143048 0.567134 0.679090 0.275869 0.6917 texture_se 0.007526 0.008303 0.097317 0.386358 0.0867 perimeter_se 0.137331 0.556141 0.674172 0.281673 0.693 area_se 0.177742 0.548236 0.735864 0.259845 0.7449 smoothness_se 0.096781 0.067016 0.222600 0.006614 0.2026 compactness_se 0.033961 0.292999 0.206000 0.191975 0.2507 concavity_se 0.055239 0.253730 0.194204 0.143293 0.2280 concave points_se 0.078768 0.408042 0.376169 0.163851 0.4072 symmetry_se 0.017306 0.006522 0.104321 0.009127 0.0816 fractal_dimension_se 0.025725 0.077972 …$

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$0 7712 df.corrwith(df['diagnosis']).abs().sort_values(ascending=false) diagnosis 1.000000 concave points_worst 0.793566 perimeter_worst 0.782914 concave points_mean 0.776614 radius_worst 0.776454 сохранение… 01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 7/11 perimeter_mean 0.742636 area_worst 0.733825 radius_mean 0.730029 area_mean 0.708984 concavity_mean 0.696360 concavity_worst 0.659610 compactness_mean 0.596534 compactness_worst 0.590998 radius_se 0.567134 perimeter_se 0.556141 area_se 0.548236 texture_worst 0.456903 smoothness_worst 0.421465 symmetry_worst 0.416294 texture_mean 0.415185 concave points_se 0.408042 smoothness_mean 0.358560 symmetry_mean 0.330499 fractal_dimension_worst 0.323872 compactness_se 0.292999 concavity_se 0.253730 fractal_dimension_se 0.077972 smoothness_se 0.067016 id 0.039769 fractal_dimension_mean 0.012838 texture_se 0.008303 symmetry_se 0.006522 dtype: float64 ml ga tayyorlaymiz x = df.drop('diagnosis', axis=1).values y = df['diagnosis'] from sklearn.preprocessing import standardscaler scaler = standardscaler() x = scaler.fit_transform(x) ▾ kneighborsclassifier kneighborsclassifier() # train/test split from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=12) # k-nn from sklearn.neighbors import kneighborsclassifier knn = kneighborsclassifier(n_neighbors=5) # k-ni qiymati knn.fit(x_train, y_train) сохранение… 01.04.2023, 10:12 k-eng yaqin qo'shni (knn) algoritmi.ipynb - colaboratory https://colab.research.google.com/drive/1b_nspvpqfaiuyxa8y3wsbiopp--p3za-#printmode=true 8/11 y_pridect=knn.predict(x_test) print(y_test.to_numpy()) …$

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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 …

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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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