Go Through an ML project

Go Through an ML project

1. Look at the big picture.

• Frame the Problem
  what exactly is the business objective
• Select a Performance Measure
  Root Mean Square Error (RMSE) - L2范式
  Mean Absolute Error (MAE) - L1范式
• Check the Assumptions

2. Get the data.

2.1 Create the Workspace

Anaconda ( Jupyter | Spyder )

2.2 Download the Data

Popular open data repositories

• UC Irvine Machine Learning Repository
• Kaggle datasets
• Amazon’s AWS datasets

Meta portals (they list open data repositories)

• http://dataportals.org/
• http://opendatamonitor.eu/
• http://quandl.com/

Other pages listing many popular open data repositories

• Wikipedia’s list of Machine Learning datasets
• Quora.com question
• Datasets subreddit

2.3 Take a Quick Look at the Data Structure

head()、info()、[‘key’].value_counts() 统计值出现的次数、describe()-shows a summary of the numerical attributes、hist()
Jupyter’s magic command “%matplotlib inline”

2.4 Create a Test Set

pick 20% of the dataset randomly, and set them aside

train_set, test_set = sklearn.model_selection.train_test_split(housing, test_size=0.2, random_state=42)
层次取样 
split = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
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3. Discover and visualize the data to gain insights.

• Visualizing Geographical Data
• Looking for Correlations
  corr() 查看各个特征与当前特征的关系
  pandas.tools.plotting.scatter_matrix() 查看n个特征两两之间的关系，并plot绘图
• Experimenting with Attribute Combinations
  such as bedrooms_per_room better than the total number of rooms

4. Prepare the data for Machine Learning algorithms.

4.1 Data Cleaning

• Get rid of the corresponding districts.
  housing.dropna(subset=[“total_bedrooms”]) # option 1
• Get rid of the whole attribute.
  housing.drop(“total_bedrooms”, axis=1) # option 2
• Set the values to some value (zero, the mean, the median, etc.)
  比如 #直接设置空值为平均数
  median = housing[“total_bedrooms”].median()
  housing[“total_bedrooms”].fillna(median) # option 3
  或者 #使用Imputer管理转换空值
  imputer = sklearn.preprocessing.Imputer(strategy=“median”)
  X = imputer.fit_transform(housing_num)
  housing_tr = pd.DataFrame(X, columns=housing_num.columns)

4.2 Handling Text and Categorical Attributes

convert these text labels to numbers

• sklearn.preprocessing.LabelEncoder将类别转换成[0, 1, 2,]
  【问题】类别值只代表某一个类，大小无意义，但是用数值可能会有误导
• sklearn.preprocessing.OneHotEncoder 将类别转换成OneHot形式
  sklearn.preprocessing.OneHotEncoder #1
  sklearn.preprocessing.LabelBinarizer #2
  #1 和 #2 都可以将数值型、文本型数据转为OneHot形式，区别是#1输入二维数组，#2输入一维数组（0.22.x版本之前categories维度判断，#1用的是Max(value)只能处理数值型，升级之后用的是Unique(value)就OK了）

4.3 Custom Transformers 订制转换

sklearn.base.BaseEstimator、sklearn.base.TransformerMixin

4.4 Feature Scaling

• min-max scaling ( subtracting the min value and dividing by the max minus the min )
  sklearn.preprocessing.MinMaxScaler
• standardization ( subtracts the mean value (so standardized values always have a zero mean), and then it divides by the variance so that the resulting distribution has unit variance )
  sklearn.preprocessing.StandardScaler

标准化对比MAX-MIN来说，异常值的容错性更大；但值域却不在[0, 1]

4.5 Transformation Pipelines

• sklearn.pipeline.Pipeline 串行Pipeline
• sklearn.pipeline.FeatureUnion 并行Pipeline
  Pipeline要求流程中出最后一个步骤以外，之前的步骤都必须是transformers，每一步都会调用fit_transform()，并将输出作为下一次输入

5. Select a model and train it.

5.1 Training and Evaluating on the Training Set

• underfitting
  select a more powerful model
  better features
  reduce the constraints(regularized) on the model

• overfitting
  simplify the model
  gather more training data
  reduce the noise in the training data
  add the constraints(regularized) on the model

5.2 Better Evaluation Using Cross-Validation

sklearn.model_selection.cross_val_score 交叉训练
an estimate of the performance of model
a measure of how precise this estimate is

5.3 model save ( sklearn.externals.joblib )

6. Fine-tune your model.

• Grid Search（超参不多）
  fiddle with the hyperparameters manually 手动调整超参
  sklearn.model_selection.GridSearchCV 设定多组超参数，自动遍历，对比
• Randomized Search（超参很多）
  sklearn.model_selection.RandomizedSearchCV 随机的选择组合超参对比
  1,000 iterations，可以迭代一千次，尝试一千个随机值，而不是指定的特定值；在有足够的资源尝新时，可能寻到更好的参数组合
• Ensemble Methods
• Analyze the Best Models and Their Errors
• Evaluate Your System on the Test Set

7. Present your solution.

• high‐lighting what you have learned
• what worked and what did not
• what assumptions were made
• what your system’s limitations are
• document everything
• create nice presentations with clear visualizations and easy-to-remember statements

8. Launch, monitor, and maintain your system.

• check your system’s live performance at regular intervals and trigger alerts when it drops
• evaluate the system’s input data quality.
• setting up human evaluation pipelines
• automating regular model training

@ WHAT - HOW - WHY