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使用遗传交叉算子进行过采样处理数据不平衡

model.predict(X_test)) balanced_accuracy = balanced_accuracy_score(y_test, model.predict(X_test)...model.predict(X_test)) balanced_accuracy = balanced_accuracy_score(y_test, model.predict(X_test)...model.predict(X_test)) balanced_accuracy = balanced_accuracy_score(y_test, model.predict(X_test)...model.predict(X_test)) balanced_accuracy = balanced_accuracy_score(y_test, model.predict(X_test)...model.predict(X_test)) balanced_accuracy = balanced_accuracy_score(y_test, model.predict(X_test)

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Python和R代码机器学习算法速查对比表

'Coefficient: \n', model.coef_) print('Intercept: \n', model.intercept_) #Predict Output predicted= model.predict...using the training sets and check #score model.fit(X, y) model.score(X, y) #Predict Outputpredicted= model.predict...using the training sets and check #score model.fit(X, y) model.score(X, y) #Predict Outputpredicted= model.predict...#Train the model using the training sets and check #scoremodel.fit(X, y) #Predict Outputpredicted= model.predict...#Train the model using the training sets and check score model.fit(X, y) #Predict Outputpredicted= model.predict

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机器学习简单小例子：19行代码预测鸢尾花品种

random_state=1) 训练数据集合测试数据集的比例是8:2 训练模型并预测 model = SVC(gamma='auto') model.fit(X_train,Y_train) predictions = model.predict...(X_test) 输入数据预测 iris = [1,1,1,1] results = model.predict([iris]) print(results) 结果results是一个列表输出模型准确性...,y,test_size=0.2,random_state=1) model = SVC(gamma='auto') model.fit(X_train,Y_train) predictions = model.predict...pd.to_pickle(model,r"new_model.pickle") model = pd.read_pickle(r"new_model.pickle") iris = [1,1,1,1] results = model.predict

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机器学习第5天：多项式回归与学习曲线

而我们的方程的一次参数和二次参数分别是3和2，可见效果还是很好的把预测的结果绘制出来 model = LinearRegression() model.fit(x_poly, y) pre_y = model.predict...m in range(1, len(x_train)): model.fit(x_train[:m], y_train[:m]) y_train_predict = model.predict...(x_train[:m]) y_val_predict = model.predict(x_val) train_errors.append(mean_squared_error...(x_train[:m]) y_val_predict = model.predict(x_val) train_errors.append(mean_squared_error...(x_train[:m]) y_val_predict = model.predict(x_val) train_errors.append(mean_squared_error

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深度学习在医疗保健领域的应用：从图像识别到疾病预测

lung_cancer_detection_model') # 对CT扫描图像进行预测 image = load_and_preprocess_image('ct_scan.png') prediction = model.predict...pathology_analysis_model') # 对组织切片图像进行预测 image = load_and_preprocess_image('pathology_slice.png') prediction = model.predict...drug_discovery_model') # 输入药物分子结构数据 molecule_data = load_molecule_data('molecule_data.csv') drug_target = model.predict...genomic_analysis_model') # 输入患者的基因组数据 genomic_data = load_genomic_data('genomic_data.csv') risk_factors = model.predict...# 输入患者的临床数据 clinical_data = load_clinical_data('clinical_data.csv') comprehensive_patient_profile = model.predict

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数据分析50+高频场景实战业绩提升立竿见影|果fx

test_size=0.2, random_state=42)# 模型训练model = LinearRegression()model.fit(X_train, y_train)# 预测y_pred = model.predict...test_size=0.2, random_state=42)# 模型训练model = LogisticRegression()model.fit(X_train, y_train)# 预测y_pred = model.predict...=0.2, random_state=42)# 模型训练model = DecisionTreeClassifier()model.fit(X_train, y_train)# 预测y_pred = model.predict...=0.2, random_state=42)# 模型训练model = RandomForestClassifier()model.fit(X_train, y_train)# 预测y_pred = model.predict

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强化学习在游戏开发中的应用：打造智能化游戏体验

action = np.random.choice(action_shape) else: action = np.argmax(model.predict...action) next_state = next_state.reshape(1, -1) target = reward + gamma * np.amax(model.predict...(next_state)) if not done else reward target_f = model.predict(state) target_f[...action) next_state = next_state.reshape(1, -1) target = reward + gamma * np.amax(model.predict...(next_state)) if not done else reward target_f = model.predict(state) target_f[

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五分钟搭建BERT服务，实现1000+QPS，这个Service-Streamer做到了

]", 'the answer to life, the [MASK], andeverything'] model=TextaInfillingModel() outputs=model.predict...naive", methods=["POST"]) def naive_predict( ): inputs =request.form.getlist("s") outputs =model.predict...from service_streamer import ThreadStreamer streamer=ThreadedStreamer (model.predict,batch_size=64, max_latency...outputs= model.predict(batch_inputs) 用service_streamer中间件封装predict函数，将request排队成一个完整的batch，再送进GPU。...from service_streamer import ThreadedStreamer streamer = ThreadedStreamer(model.predict, 64, 0.1) xs

2.3K4 0

第 03 课：开发您的第一个 XGBoost 模型

model.fit（）**函数来训练XGBoost 模型进行分类： model = XGBClassifier() model.fit(X_train, y_train) 然后可以通过在新数据上调用**model.predict...y_pred = model.predict(X_test) 我们可以将这些结合起来如下： # First XGBoost model for Pima Indians dataset from numpy...data model = XGBClassifier() model.fit(X_train, y_train) # make predictions for test data y_pred = model.predict

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模型的可解释性：部分依赖图PDP和个体条件期望图ICE

预测函数 (model.predict)。数据集 (X100)。是否制作部分依赖图或个体条件期望图。...=True, feature_expected_value=True ) 函数会迭代 X100 中的所有样本，并且对于每个样本多次调用 model.predict 函数，修改目标特征的值，但保持补充特征...shap.plots.partial_dependence( "MedInc", model.predict, X100, ice=False, model_expected_value...shap.plots.partial_dependence( "AveOccup", model.predict, X100, ice=True, model_expected_value...shap.plots.partial_dependence( "MedInc", model.predict, X100, ice=True, model_expected_value=

1.4K5 0

模型的可解释性：部分依赖图PDP和个体条件期望图ICE

预测函数 (model.predict)。数据集 (X100)。是否制作部分依赖图或个体条件期望图。...shap.plots.partial_dependence( "AveOccup", model.predict, X100, ice=False, model_expected_value=True...shap.plots.partial_dependence( "MedInc", model.predict, X100, ice=False, model_expected_value=True,...shap.plots.partial_dependence( "AveOccup", model.predict, X100, ice=True, model_expected_value=True...shap.plots.partial_dependence( "MedInc", model.predict, X100, ice=True, model_expected_value=True,

2.5K3 0

一元回归分析

初始化模型x1 = x.reshape(-1,1) # 将行变列得到x坐标y1 = y.reshape(-1,1) # 将行变列得到y坐标model.fit(x1,y1) #训练数据model.predict...plt.figure(figsize=(12,8)) plt.scatter(x,y) x_test = np.linspace(0,40).reshape(-1,1) plt.plot(x_test,model.predict...0.86175551]) 截距 y = 1.00116024 * x + 0.86175551 那怎么评价这个模型的好坏当然是越靠近越好不就是点到直线的距离的平方越小越好 np.sum(np.square(model.predict...np.square(y2 - y1)) # 16.64430773735106 还是画个图 plt.figure(figsize=(10,10)) plt.scatter(x1,y1) plt.plot(x1,model.predict

1.1K3 0

科大讯飞：电信客户流失预测赛方案

(val_x, num_iteration=model.best_iteration) test_pred = model.predict(test_x, num_iteration...num_boost_round=50000, evals=watchlist, verbose_eval=3000, early_stopping_rounds=200) val_pred = model.predict...(valid_matrix, ntree_limit=model.best_ntree_limit) test_pred = model.predict(test_matrix...cat_features=[], use_best_model=True, verbose=3000) val_pred = model.predict...(val_x) test_pred = model.predict(test_x) train[valid_index] = val_pred

1.7K1 0

五分钟搭建BERT服务，实现1000+QPS

the answer to life, the [MASK], and everything ] model=TextaInfillingModel() outputs=model.predict..., methods=["POST"]) def naive_predict( ): inputs = request.form.getlist("s") outputs = model.predict...from service_streamer import ThreadStreamer streamer = ThreadedStreamer (model.predict, batch_size...outputs = model.predict(batch_inputs) 用service_streamer中间件封装predict函数，将request 排队成一个完整的batch，再送进GPU...from service_streamer import ThreadedStreamer streamer = ThreadedStreamer(model.predict, 64, 0.1) xs

3.5K2 1

探秘机器学习算法：智慧背后的代码逻辑

np.array([1, 2, 1.3, 3.75, 2.25]) # 创建模型并训练 model = LinearRegression() model.fit(X, y) # 预测 predicted = model.predict...np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) Z = model.predict...np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) Z = model.predict...np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) Z = model.predict...np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) Z = model.predict

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报错:ValueError: Expected 2D array, got scalar array instead

报错代码： new_x = 84610 pre_y = model.predict(new_x) print(pre_y) 报错结果： ValueError: Expected 2D array, got...np.array(new_x).reshape(1, -1) 修改后的代码： new_x = 84610 new_x = np.array(new_x).reshape(1, -1) pre_y = model.predict

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机器学习之决策树（下）

(x_train) print('训练集正确率：', accuracy_score(y_train, y_train_pred)) y_test_hat = model.predict(x_test)...'#A0FFA0', '#FFA0A0', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) y_show_hat = model.predict...生成网格采样点 x_show = np.stack((x1.flat, x2.flat), axis=1) # 测试点 # 训练集上的预测结果 y_train_pred = model.predict...(x_train) acc_train = accuracy_score(y_train, y_train_pred) y_test_pred = model.predict(x_test...A0FFA0', '#FFA0A0', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) y_hat = model.predict

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scikit-learn实战1：回归问题

tr = int(size*i*0.1) model.fit(data[:tr].reshape(-1,1), label[:tr].reshape(-1,1)) res = model.predict...include_bias=True), LinearRegression()) # 循环选择次数为1-9的依次测试 model.fit(train_x, train_y) res = model.predict...(train_x) # 得到在训练集上的预测结果 res2 = model.predict(data.reshape(size,1)) # 预测整个数据集的结果，用于画图 score.append...res)) # 数据可视化，红色为训练集上的预测，绿色为测试集上的预测 plt.plot(data,label,color = 'blue',linewidth = 2) # 得到源数据图像 y = model.predict...(PolynomialFeatures(best, include_bias=True), LinearRegression()) model.fit(X_train, y_train) res = model.predict

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什么是偏拟合和什么是过拟合，解决方法是什么

LinearRegression # 训练线性回归模型 model = LinearRegression() model.fit(X, y) # 可视化拟合结果 plt.scatter(X, y) plt.plot(X, model.predict...() model.fit(X_poly, y_combined) # 绘制拟合结果 plt.scatter(X_combined, y_combined) plt.plot(X_combined, model.predict...创建线性回归模型 model = LinearRegression() # 训练模型 model.fit(X_poly, y) # 绘制拟合结果 plt.scatter(X, y) plt.plot(X, model.predict...创建线性回归模型 model = LinearRegression() # 训练模型 model.fit(X_poly, y) # 绘制拟合结果 plt.scatter(X, y) plt.plot(X, model.predict...Ridge(alpha=0.1) # 设置正则化参数alpha # 训练模型 model.fit(X_poly, y) # 绘制拟合结果 plt.scatter(X, y) plt.plot(X, model.predict

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python——pycharm进行统计建模

from sklearn.metrics import mean_squared_error, r2_score, accuracy_score, roc_auc_score y_pred = model.predict...model.coef_}) coefficients.sort_values(by='Coefficient', ascending=False) sns.residplot(y=y, y_pred=model.predict...new_data = pd.read_csv('new_data.csv') new_X = new_data[['feature1', 'feature2']] new_y_pred = model.predict

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使用遗传交叉算子进行过采样处理数据不平衡

Python和R代码机器学习算法速查对比表

机器学习简单小例子：19行代码预测鸢尾花品种

机器学习第5天：多项式回归与学习曲线

深度学习在医疗保健领域的应用：从图像识别到疾病预测

数据分析50+高频场景实战业绩提升立竿见影|果fx

强化学习在游戏开发中的应用：打造智能化游戏体验

五分钟搭建BERT服务，实现1000+QPS，这个Service-Streamer做到了

第 03 课：开发您的第一个 XGBoost 模型

模型的可解释性：部分依赖图PDP和个体条件期望图ICE

模型的可解释性：部分依赖图PDP和个体条件期望图ICE

一元回归分析

科大讯飞：电信客户流失预测赛方案

五分钟搭建BERT服务，实现1000+QPS

探秘机器学习算法：智慧背后的代码逻辑

报错:ValueError: Expected 2D array, got scalar array instead

机器学习之决策树（下）

scikit-learn实战1：回归问题

什么是偏拟合和什么是过拟合，解决方法是什么

python——pycharm进行统计建模

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