我们因为之前只介绍了Logistic Regression这一种分类算法。所以本次的问题解决过程和优化思路,都集中在这种算法上。 3.初探数据 先看看我们的数据,长什么样吧。在Data下我们train.csv和test.csv两个文件,分别存着官方给的训练和测试数据。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importpa
那么接下来就是训练以及评估我们的model，我们需要根据项目的一个target，比如说它是classification还是regression，还是其他来选择对应的model在训练集上进行训练，并且在测试集上评估模型的表现，我们并且需要争取不断的优化这个对应的metric。那么到此为止我们已经走完了一个Kaggle data项目的基本流程，实现了用我们ML的mod...
数据脚本地址:https://www.dataquest.io/blog/large_files/gen_data.py。数据如下: import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline ### Import data # Always good to set a seed for reproducibility SEED = 222 np.random.seed(SEED) df = pd.read_csv('...
{ 'application': 'regression', 'boosting': 'gbdt', 'metric': 'rmse', 'num_leaves': num_leaves, 'max_depth': max_depth, 'max_bin': max_bin, 'bagging_fraction': bagging_fraction, 'bagging_freq': bagging_freq, 'feature_fraction': feature_fraction, 'min_split_gain': min_split_gain...
for col1, col2 in itertools.combinations(cat_features, 2): new_col_name = '_'.join([col1, col2]) # Convert to strings and combine new_values = ks[col1].map(str) + "_" + ks[col2].map(str) label_enc = LabelEncoder() ...
data_train.Age[data_train.Pclass == 3].plot(kind='kde') plt.xlabel(u"年龄")# plots an axis lable plt.ylabel(u"密度") plt.title(u"各等级的乘客年龄分布") plt.legend((u'头等舱', u'2等舱',u'3等舱'),loc='best') # sets our legend for our graph. ...
导入sklearn.linear_model中的LinearRegression 模型评估:在常用的回归评估指标包括: r2_score explained_variance_score 这里使用的是r2_score R2 决定系数(拟合优度) 模型越好:r2→1 模型越差:r2→0 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from sklearn.linear_model import LinearRegression lr = ...
Support vector machines are supervised learning models that are used for classification and regression analysis. This model was chosen with the parameters below. You should be trying other models with different parameter combinations. There is no need to start and stop the code every time you want...
linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV # Figures inline and set visualization style %matplotlib inline sns.set() # Import data df_train = pd.read_csv('data/train.csv') df_test = pd.read_csv('data/test.csv') Powered By Below, you will ...
{ 'application': 'regression', 'boosting': 'gbdt', 'metric': 'rmse', 'num_leaves': num_leaves, 'max_depth': max_depth, 'max_bin': max_bin, 'bagging_fraction': bagging_fraction, 'bagging_freq': bagging_freq, 'feature_fraction': feature_fraction, 'min_split_gain': min_split_gain...