From 94a76f47d8ba0a211cfceeff6ab16df3bd1e2f55 Mon Sep 17 00:00:00 2001 From: DmitriyAntonov Date: Tue, 19 Sep 2023 10:23:19 +0400 Subject: [PATCH 1/6] start --- antonov_dmitry_lab_1/README.md | 14 ++++++++ antonov_dmitry_lab_1/lab1.py | 59 ++++++++++++++++++++++++++++++++++ 2 files changed, 73 insertions(+) create mode 100644 antonov_dmitry_lab_1/README.md create mode 100644 antonov_dmitry_lab_1/lab1.py diff --git a/antonov_dmitry_lab_1/README.md b/antonov_dmitry_lab_1/README.md new file mode 100644 index 0000000..4820d94 --- /dev/null +++ b/antonov_dmitry_lab_1/README.md @@ -0,0 +1,14 @@ +# Лаб 1 +Работа с типовыми наборами данных и различными моделями +# Вариант 3 +Данные: make_classification (n_samples=500, n_features=2, +n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) +# Модели: +1. Линейную регрессию +1. Полиномиальную регрессию (со степенью 3) +1. Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0) +# Screenshots + +

+ +

\ No newline at end of file diff --git a/antonov_dmitry_lab_1/lab1.py b/antonov_dmitry_lab_1/lab1.py new file mode 100644 index 0000000..68139e6 --- /dev/null +++ b/antonov_dmitry_lab_1/lab1.py @@ -0,0 +1,59 @@ +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.colors import ListedColormap +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.datasets import make_moons, make_circles, make_classification +from sklearn.neural_network import MLPClassifier + +X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=0, n_clusters_per_class=1) +rng = np.random.RandomState(2) +X += 2 * rng.uniform(size=X.shape) +linearly_dataset = (X, y) +moon_dataset = make_moons(noise=0.3, random_state=0) +circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1) + +datasets = [moon_dataset, circles_dataset, linearly_dataset] +for ds_cnt, ds in enumerate(datasets): + X, y = ds + X = StandardScaler().fit_transform(X) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42) + + alphas = np.logspace(-5, 3, 5) + + current_subplot = plt.subplot(3, 5 + 1, i) + +current_subplot.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) +current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) + +cm = plt.cm.RdBu +cm_bright = ListedColormap(['#FF0000', '#0000FF']) + +h = .02 # шаг регулярной сетки +x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5 +x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5 +xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h)) + +Z = clf.decision_function(np.c_[xx0.ravel(), xx1.ravel()]) # 1 +Z = clf.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1] # 2 +Z = clf.predict(np.c_[xx0.ravel(), xx1.ravel()]) # 3 + +hasattr(clf, "decision_function") + +Z = Z.reshape(xx0.shape) +current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8) +current_subplot.set_xlim(xx0.min(), xx0.max()) +current_subplot.set_ylim(xx0.min(), xx1.max()) +current_subplot.set_xticks(()) +current_subplot.set_yticks(()) +current_subplot.set_title(name) +current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'), + size=15, horizontalalignment='right') + +current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) + +figure.subplots_adjust(left=.02, right=.98) + +current_subplot.set_title("Input data") +current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'), size=15, + horizontalalignment='right') From 9bf1c4845a1854a09b59e5805bf686da88ebcab0 Mon Sep 17 00:00:00 2001 From: DmitriyAntonov Date: Sat, 23 Sep 2023 17:22:03 +0400 Subject: [PATCH 2/6] iter1 --- antonov_dmitry_lab_1/lab1.py | 112 +++++++++++++++++++++++------------ 1 file changed, 75 insertions(+), 37 deletions(-) diff --git a/antonov_dmitry_lab_1/lab1.py b/antonov_dmitry_lab_1/lab1.py index 68139e6..f92c5d4 100644 --- a/antonov_dmitry_lab_1/lab1.py +++ b/antonov_dmitry_lab_1/lab1.py @@ -1,59 +1,97 @@ import numpy as np from matplotlib import pyplot as plt -from matplotlib.colors import ListedColormap -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler +from skimage.metrics import mean_squared_error from sklearn.datasets import make_moons, make_circles, make_classification -from sklearn.neural_network import MLPClassifier +from sklearn.linear_model import LinearRegression, Ridge +from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler, PolynomialFeatures + +X, y = make_classification( + n_features=2, + n_redundant=0, + n_informative=2, + random_state=0, + n_clusters_per_class=1 +) -X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=0, n_clusters_per_class=1) rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape) linearly_dataset = (X, y) moon_dataset = make_moons(noise=0.3, random_state=0) circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1) - datasets = [moon_dataset, circles_dataset, linearly_dataset] + +""" +Данные: +· moon_dataset +· circles_dataset +· linearly_dataset +""" for ds_cnt, ds in enumerate(datasets): X, y = ds X = StandardScaler().fit_transform(X) - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42) + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=.4, random_state=42 + ) + """ + Модели: + · Линейную регрессию + · Полиномиальную регрессию (со степенью 3) + · Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0) + """ - alphas = np.logspace(-5, 3, 5) + # Линейная + linear_regression = LinearRegression() + linear_regression.fit(X_train, y_train) + linear_predictions = linear_regression.predict(X_test) + linear_mse = mean_squared_error(y_test, linear_predictions) - current_subplot = plt.subplot(3, 5 + 1, i) + # Полиномиальная (degree=3) + poly_regression = make_pipeline(PolynomialFeatures(degree=3), LinearRegression()) + poly_regression.fit(X_train, y_train) + poly_predictions = poly_regression.predict(X_test) + poly_mse = mean_squared_error(y_test, poly_predictions) -current_subplot.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) -current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) + # Гребневая (degree=3, alpha=1.0) + poly_regression_alpha = make_pipeline(PolynomialFeatures(degree=3), Ridge(alpha=1.0)) + poly_regression_alpha.fit(X_train, y_train) + poly_alpha_predictions = poly_regression_alpha.predict(X_test) + poly_alpha_mse = mean_squared_error(y_test, poly_alpha_predictions) -cm = plt.cm.RdBu -cm_bright = ListedColormap(['#FF0000', '#0000FF']) + # График данных + plt.figure(figsize=(10, 6)) + plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='coolwarm') + plt.title('Датасет №' + str(ds_cnt)) + plt.xlabel('X') + plt.ylabel('Y') -h = .02 # шаг регулярной сетки -x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5 -x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5 -xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h)) + # График линейной модели + plt.figure(figsize=(10, 6)) + plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_predictions, cmap='coolwarm') + plt.title('Линейная ds'+ str(ds_cnt)) + plt.xlabel('X') + plt.ylabel('Y') + plt.show() -Z = clf.decision_function(np.c_[xx0.ravel(), xx1.ravel()]) # 1 -Z = clf.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1] # 2 -Z = clf.predict(np.c_[xx0.ravel(), xx1.ravel()]) # 3 + # График полиномиальной модели (degree=3) + plt.figure(figsize=(10, 6)) + plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_predictions, cmap='coolwarm') + plt.title('Полиномиальная (degree=3) ds' + str(ds_cnt)) + plt.xlabel('X') + plt.ylabel('Y') + plt.show() -hasattr(clf, "decision_function") + # График гребневой модели (degree=3, alpha=1.0) + plt.figure(figsize=(10, 6)) + plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_alpha_predictions, cmap='coolwarm') + plt.title('Гребневая (degree=3, alpha=1.0) ds' + str(ds_cnt)) + plt.xlabel('X') + plt.ylabel('Y') + plt.show() -Z = Z.reshape(xx0.shape) -current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8) -current_subplot.set_xlim(xx0.min(), xx0.max()) -current_subplot.set_ylim(xx0.min(), xx1.max()) -current_subplot.set_xticks(()) -current_subplot.set_yticks(()) -current_subplot.set_title(name) -current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'), - size=15, horizontalalignment='right') + # Сравнение качества + print('Линейная MSE:', linear_mse) + print('Полиномиальная (degree=3) MSE:', poly_mse) + print('Гребневая (degree=3, alpha=1.0) MSE:', poly_alpha_mse) -current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) - -figure.subplots_adjust(left=.02, right=.98) - -current_subplot.set_title("Input data") -current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'), size=15, - horizontalalignment='right') From 3543ab516319138b5c297dba0f16a2febaae4dda Mon Sep 17 00:00:00 2001 From: DmitriyAntonov Date: Sat, 23 Sep 2023 17:42:04 +0400 Subject: [PATCH 3/6] iter2 --- antonov_dmitry_lab_1/README.md | 54 +++++++++++++++++++++- antonov_dmitry_lab_1/lab1.py | 6 +-- antonov_dmitry_lab_1/screens/myplot1.png | Bin 0 -> 18607 bytes antonov_dmitry_lab_1/screens/myplot10.png | Bin 0 -> 18559 bytes antonov_dmitry_lab_1/screens/myplot11.png | Bin 0 -> 20679 bytes antonov_dmitry_lab_1/screens/myplot12.png | Bin 0 -> 21195 bytes antonov_dmitry_lab_1/screens/myplot2.png | Bin 0 -> 20019 bytes antonov_dmitry_lab_1/screens/myplot3.png | Bin 0 -> 21922 bytes antonov_dmitry_lab_1/screens/myplot4.png | Bin 0 -> 22427 bytes antonov_dmitry_lab_1/screens/myplot5.png | Bin 0 -> 20499 bytes antonov_dmitry_lab_1/screens/myplot6.png | Bin 0 -> 19570 bytes antonov_dmitry_lab_1/screens/myplot7.png | Bin 0 -> 23394 bytes antonov_dmitry_lab_1/screens/myplot8.png | Bin 0 -> 23946 bytes antonov_dmitry_lab_1/screens/myplot9.png | Bin 0 -> 19250 bytes 14 files changed, 55 insertions(+), 5 deletions(-) create mode 100644 antonov_dmitry_lab_1/screens/myplot1.png create mode 100644 antonov_dmitry_lab_1/screens/myplot10.png create mode 100644 antonov_dmitry_lab_1/screens/myplot11.png create mode 100644 antonov_dmitry_lab_1/screens/myplot12.png create mode 100644 antonov_dmitry_lab_1/screens/myplot2.png create mode 100644 antonov_dmitry_lab_1/screens/myplot3.png create mode 100644 antonov_dmitry_lab_1/screens/myplot4.png create mode 100644 antonov_dmitry_lab_1/screens/myplot5.png create mode 100644 antonov_dmitry_lab_1/screens/myplot6.png create mode 100644 antonov_dmitry_lab_1/screens/myplot7.png create mode 100644 antonov_dmitry_lab_1/screens/myplot8.png create mode 100644 antonov_dmitry_lab_1/screens/myplot9.png diff --git a/antonov_dmitry_lab_1/README.md b/antonov_dmitry_lab_1/README.md index 4820d94..5af24ba 100644 --- a/antonov_dmitry_lab_1/README.md +++ b/antonov_dmitry_lab_1/README.md @@ -1,14 +1,64 @@ # Лаб 1 + Работа с типовыми наборами данных и различными моделями + # Вариант 3 + Данные: make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) + # Модели: + 1. Линейную регрессию 1. Полиномиальную регрессию (со степенью 3) 1. Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0) -# Screenshots + +# Графики

- +

Набор лун (moon_dataset)
+ +

+

+

Графики регрессии
+ + + +
+ Линейная MSE: 0.0936 + Полиномиальная (degree=3) MSE: 0.0674 + Гребневая (degree=3, alpha=1.0) MSE: 0.0682 +
+

+ +

+

Набор кругов (circles_dataset)
+ +

+

+

Графики регрессии
+ + + +
+ Линейная MSE: 0.2684 + Полиномиальная (degree=3) MSE: 0.1341 + Гребневая (degree=3, alpha=1.0) MSE: 0.1312 +
+

+ +

+

Набор линейный (linearly_dataset)
+ +

+

+

Графики регрессии
+ + + +
+ Линейная MSE: 0.1101 + Полиномиальная (degree=3) MSE: 0.1045 + Гребневая (degree=3, alpha=1.0) MSE: 0.1078 +

\ No newline at end of file diff --git a/antonov_dmitry_lab_1/lab1.py b/antonov_dmitry_lab_1/lab1.py index f92c5d4..53e49d1 100644 --- a/antonov_dmitry_lab_1/lab1.py +++ b/antonov_dmitry_lab_1/lab1.py @@ -68,7 +68,7 @@ for ds_cnt, ds in enumerate(datasets): # График линейной модели plt.figure(figsize=(10, 6)) - plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_predictions, cmap='coolwarm') + plt.scatter(X_test[:, 0], linear_predictions, c=linear_predictions, cmap='coolwarm') plt.title('Линейная ds'+ str(ds_cnt)) plt.xlabel('X') plt.ylabel('Y') @@ -76,7 +76,7 @@ for ds_cnt, ds in enumerate(datasets): # График полиномиальной модели (degree=3) plt.figure(figsize=(10, 6)) - plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_predictions, cmap='coolwarm') + plt.scatter(X_test[:, 0], poly_predictions, c=poly_predictions, cmap='coolwarm') plt.title('Полиномиальная (degree=3) ds' + str(ds_cnt)) plt.xlabel('X') plt.ylabel('Y') @@ -84,7 +84,7 @@ for ds_cnt, ds in enumerate(datasets): # График 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+Качество каждой модели может быть оценено на основе среднеквадратичной ошибки (MSE). +Более низкий MSE указывает на лучшее соответствие данным. +Однако выбор модели зависит от набора данных и лежащей в основе взаимосвязи между объектами и целевой переменной. + +Линейная регрессия: Линейная регрессия предполагает линейную зависимость между признаками и целевой переменной. +Это хорошо работает, когда взаимосвязь линейна, а шум в наборе данных минимален. +Лучше всего сработала на наборе лун. Хуже всего на кругах. +На линейном наборе показала себя на равне с остальными. + +Полиномиальная и гребневая показали примерно одинаково на всех наборах. + +Полиномиальная регрессия (степень=3): +Полиномиальная регрессия обеспечивает более гибкую подгонку за счет полинома более высокого порядка(кубическая кривая). +Она может выявить более сложные взаимосвязи между объектами и целевой переменной. +Она может сработать лучше, чем линейная регрессия, если истинная взаимосвязь нелинейна. + +Гребневая регрессия (степень= 3, альфа=1,0): +В случае полиномиальной регрессии с регуляризацией (альфа=1,0) модель добавляет коэффициент регуляризации +для управления сложностью обучения. Регуляризация помогает предотвратить переобучение, когда набор +данных содержит шум или когда он ограничен. +
+

Набор лун (moon_dataset)
- +

Графики регрессии
- - - + + +
Линейная MSE: 0.0936 Полиномиальная (degree=3) MSE: 0.0674 @@ -33,13 +56,13 @@ n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)

Набор кругов (circles_dataset)
- +

Графики регрессии
- - - + + +
Линейная MSE: 0.2684 Полиномиальная (degree=3) MSE: 0.1341 @@ -49,13 +72,13 @@ n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)

Набор линейный (linearly_dataset)
- +

Графики регрессии
- - - + + +
Линейная MSE: 0.1101 Полиномиальная (degree=3) MSE: 0.1045 diff --git a/antonov_dmitry_lab_1/lab1.py b/antonov_dmitry_lab_1/lab1.py index 53e49d1..f92c5d4 100644 --- a/antonov_dmitry_lab_1/lab1.py +++ b/antonov_dmitry_lab_1/lab1.py @@ -68,7 +68,7 @@ for ds_cnt, ds in enumerate(datasets): # График линейной модели plt.figure(figsize=(10, 6)) - plt.scatter(X_test[:, 0], linear_predictions, c=linear_predictions, cmap='coolwarm') + plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_predictions, cmap='coolwarm') plt.title('Линейная ds'+ str(ds_cnt)) plt.xlabel('X') plt.ylabel('Y') @@ -76,7 +76,7 @@ for ds_cnt, ds in enumerate(datasets): # График полиномиальной модели (degree=3) plt.figure(figsize=(10, 6)) - plt.scatter(X_test[:, 0], poly_predictions, c=poly_predictions, cmap='coolwarm') + plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_predictions, cmap='coolwarm') plt.title('Полиномиальная (degree=3) ds' + str(ds_cnt)) plt.xlabel('X') plt.ylabel('Y') @@ -84,7 +84,7 @@ for ds_cnt, ds in enumerate(datasets): # График гребневой модели (degree=3, alpha=1.0) plt.figure(figsize=(10, 6)) - plt.scatter(X_test[:, 0], poly_alpha_predictions, c=poly_alpha_predictions, cmap='coolwarm') + plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_alpha_predictions, cmap='coolwarm') plt.title('Гребневая (degree=3, alpha=1.0) ds' + str(ds_cnt)) plt.xlabel('X') plt.ylabel('Y') diff --git a/antonov_dmitry_lab_1/screens/myplot10.png b/antonov_dmitry_lab_1/screens/myplot10.png index 51ffd379a4e0abbb2cf5ff9e3a8729ae13a43d2c..ad58249b8786102e03bf5f45a0684c5732737c7e 100644 GIT binary patch literal 18826 zcmeHvc|6qZzxPOzY^@|&B2>0YvJVw06-jo6iV`ulY%{jVUW%l$jHD#Xl%26DVb2j3rR^cR zFP#@;IdXJ&)HCa--pP2X(rc?%CDDPN4aB<>NY&Q0vCH2)bt;JOd}I>iT~9NBPPlp` zj4^^=h$4R_ANUE`7N`V%b~EjC2S0+lcH_a1+I~SSc-ZflS|ROOT|Y-=@RJO|aeyCn zhX4QKe`IoOt}QGqBvhKY4?HB6<^O83pb&L@SEF0*HZ?XDkA#VzK7D$OZ#9G}J2Emd z_U_h1E6SI;L9*~1k1@x%l}@~|tBn+}xOwv^d>bVE2$ms9mZ{RbWWaz>%L{)n={Qv8 zjUWt`GFr$)@T-_WGxY;;KE2klD#UtW6q0z^*_p^JfD|lSFZ}iWicY-z5dtGbe6tlv zqt9x&Q(5MiN18#L0)yXhQt}Dk7@0X-D-6?w*+`%>ZJc^~dNq&rQnlGtc0vp;6f&yW zc@auU>bLO&7&#C1UG8aS7QPz~oz!Ppkgs06I0{`~Uu+HF4dhqxir|qEUBNAk)}g$n z)0(8qOI#+ud9N*d&*k+f^Xm-Xgu!6LOO)XWu|$vahgo3!E1cVs0|I!pw6xCqUJ2&l z^P2rqmsPsl=P*!g?$&Gl?&6ca0=R`^&S%ihb79S8{J>hg%Qu#ikG|tEhCvQH^^J`c z9I%Dq(q%^L!XfD#{K^Mc?Gltdi49_?5-`c%RH^H}RFW?h$LU)L8r{*;Bf~a|b!k07 z?If~5KJNuGZPF13FFW3MMqFInBjvTDs`FUL&pXdko(E$-E)#r6nB*AMmEiz*8TsyX zXPSAj>z*_|dGW_<%X9r2u<%v^&%)G{loBw_7RZIi9`|~)tvQwF{Z{yPP@O)dr)xYW z5r%vhMruq890v03`y+jRz57sU-nl$iFeK%J=t$L}uFjxqq@9+!41*~q{?d?%mblpX z_!1ur6rizI8;eJM|J7xY)pUaDZLDit~ZK=0G1PdnHT9W68a z())m@K%cror%}q>5Jl315Z3Ej7@nmv8Sj$sFrZjsF#JFe8!KTv!m{TOKGwpATuK_` z^)fh$DI6PcAClz_!pMKmG%dhlvGbe4JWt#wrX5(DD=$Pe33Q(PmY~#ynM_1%yB8iF zUbCP?D3K8p(>zo9W4!}K$xU+SPcW;~=^Dr4o;+UD6!6|(a{Y{x z%~cE6c8#w}6O2;j>%=xkZZ1g^*KZ1wNHFFRqA9#IFU?VPR2+YfOePab7PW_YjRKe% zy9VE1;a(6?uZKZCOVPGS3lOm*mA`UYp{y!kCL&cSg+JHIsT+i^>^BPKTp}^Yh)q(M z+0Hb*x4e(GSu5YF+`gwf-5_(cvFx zf;Xt(qnPX?|S0(E^cfVv+7p?&+SKO^(SCx=8n-3-jQb?|L!Wz`1Q zz@-bdyrnR>(Phm$qJDKq7i zD`#@W#`_GwY#Zi7VgS%Y>o* zU`Hjy#2i@9Q(uh~P^B=du6%vW4f_H17X#ytkWpD#S$|d~S<#h#2*gh4|G~r`T0xdQ zdh{qPEKCb{0<8NKjwWA9d2V*ah7FhHfABiFE(}~Y(jUI3IvTOloxPen)}JS)e0n929(5qF)xlQfT-tDfE{IEHgzlWb75uXoM>b#cb9Y8a z+iu;IKw7UgntzZmh5_ij;H)kcYAc01wGqPru?eAn;qHTjuJ=6hm<<}AsK)&LMVg-` zdG5~{+#^${zXQ^?SN-|k%{e+10M}-MbMq)vgBfBoCBQjui%J;ApE=qdb8v+SD z682&@Z5}rV`+@3iHq3l-&etbQa`AHsBgC(kK8Al|C0Z_DzRV+Q+xb2gcH*KfFn`1; 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Subject: [PATCH 5/6] iter4 --- antonov_dmitry_lab_1/README.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/antonov_dmitry_lab_1/README.md b/antonov_dmitry_lab_1/README.md index 7ddcc82..497650e 100644 --- a/antonov_dmitry_lab_1/README.md +++ b/antonov_dmitry_lab_1/README.md @@ -17,7 +17,7 @@ n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)

Качество каждой модели может быть оценено на основе среднеквадратичной ошибки (MSE). -Более низкий MSE указывает на лучшее соответствие данным. +Более низкая MSE указывает на лучшее соответствие данным. Однако выбор модели зависит от набора данных и лежащей в основе взаимосвязи между объектами и целевой переменной. Линейная регрессия: Линейная регрессия предполагает линейную зависимость между признаками и целевой переменной. @@ -84,4 +84,10 @@ n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) Полиномиальная (degree=3) MSE: 0.1045 Гребневая (degree=3, alpha=1.0) MSE: 0.1078
-

\ No newline at end of file +

+ +
+Итоговая модель подбирается учитывая зависимость в данных, +как правило полиномиальная регрессия справляется лучше, а коэф регуляризации в гребневой регрессии помогает избежать +переобучения. +
\ No newline at end of file From d30caee3dbe9a8d2e50da39053a0920362c8143a Mon Sep 17 00:00:00 2001 From: DmitriyAntonov Date: Sat, 23 Sep 2023 21:04:51 +0400 Subject: [PATCH 6/6] iter5 --- antonov_dmitry_lab_1/README.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/antonov_dmitry_lab_1/README.md b/antonov_dmitry_lab_1/README.md index 497650e..be111c3 100644 --- a/antonov_dmitry_lab_1/README.md +++ b/antonov_dmitry_lab_1/README.md @@ -7,11 +7,15 @@ Данные: make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) +# Запуск + +Выполнением скрипта файла (вывод в консоль + рисует графики). + # Модели: -1. Линейную регрессию -1. Полиномиальную регрессию (со степенью 3) -1. Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0) +1. Линейная регрессия +1. Полиномиальная регрессия (со степенью 3) +1. Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0) # Графики

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++++++++++++++++------ antonov_dmitry_lab_1/lab1.py | 6 +-- antonov_dmitry_lab_1/screens/myplot10.png | Bin 18559 -> 18826 bytes antonov_dmitry_lab_1/screens/myplot11.png | Bin 20679 -> 21064 bytes antonov_dmitry_lab_1/screens/myplot12.png | Bin 21195 -> 21594 bytes antonov_dmitry_lab_1/screens/myplot2.png | Bin 20019 -> 18123 bytes antonov_dmitry_lab_1/screens/myplot3.png | Bin 21922 -> 19916 bytes antonov_dmitry_lab_1/screens/myplot4.png | Bin 22427 -> 20427 bytes antonov_dmitry_lab_1/screens/myplot6.png | Bin 19570 -> 19578 bytes antonov_dmitry_lab_1/screens/myplot7.png | Bin 23394 -> 22254 bytes antonov_dmitry_lab_1/screens/myplot8.png | Bin 23946 -> 22774 bytes 11 files changed, 38 insertions(+), 15 deletions(-) diff --git a/antonov_dmitry_lab_1/README.md b/antonov_dmitry_lab_1/README.md index 5af24ba..7ddcc82 100644 --- a/antonov_dmitry_lab_1/README.md +++ b/antonov_dmitry_lab_1/README.md @@ -15,15 +15,38 @@ n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) # Графики +