Use the credictcard-reduced.csv dataset ([Data description] and build Five classification models. Please plot confusion matrix, and evaluate your model performance using accuracy, precision, recall, F-score (70 points). A list of classification models can be found我现在需要完成上面的任务。已知导入的数据是这样的 Time V1 V2 V3 V4 V5 V6 V7 V8 V9 ... V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 406 -2.312227 1.951992 -1.609851 3.997906 -0.522188 -1.426545 -2.537387 1.391657 -2.770089 ... 0.517232 -0.035049 -0.465211 0.320198 0.044519 0.177840 0.261145 -0.143276 0.00 1 names = [
"Nearest Neighbors",
"Linear SVM",
"RBF SVM",
"Gaussian Process",
"Decision Tree",
"Random Forest",
"Neural Net",
"AdaBoost",
"Naive Bayes",
"QDA",
]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025, random_state=42),
SVC(gamma=2, C=1, random_state=42),
GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),
DecisionTreeClassifier(max_depth=5, random_state=42),
RandomForestClassifier(
max_depth=5, n_estimators=10, max_features=1, random_state=42
),
MLPClassifier(alpha=1, max_iter=1000, random_state=42),
AdaBoostClassifier(random_state=42),
GaussianNB(),
QuadraticDiscriminantAnalysis(),
]
X, y = make_classification(
n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [
make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable,
]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds_cnt, ds in enumerate(datasets):
# preprocess dataset, split into training and test part
X, y = ds
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(["#FF0000", "#0000FF"])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
# Plot the testing points
ax.scatter(
X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf = make_pipeline(StandardScaler(), clf)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
DecisionBoundaryDisplay.from_estimator(
clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
)
# Plot the training points
ax.scatter(
X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
)
# Plot the testing points
ax.scatter(
X_test[:, 0],
X_test[:, 1],
c=y_test,
cmap=cm_bright,
edgecolors="k",
alpha=0.6,
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(
x_max - 0.3,
y_min + 0.3,
("%.2f" % score).lstrip("0"),
size=15,
horizontalalignment="right",
)
i += 1
plt.tight_layout()
plt.show() 老师给的模板是这样的，请帮我写类似的代码
本文探讨了图的尺寸表示方法，使用m和n分别代表图的长和宽，单位为英尺。此外，文章还介绍了121概念，用于描述图的行列数及其子图的具体位置。
扫一扫
1946
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
