regression 回归:

最小化误差平方和

  • 最小二乘法

  • 梯度下降法

    根据训练数据找出决策边界 from sklearn import tree clf = tree.DecisionTreeClassifier() clf = clf.fit(X, Y) clf.predict(test_feature)

  • modules tree

outliers 异常值:

  • outlier detection train->remove(reduce data set,去除最大误差值的点)->train again 如此可以多次重复

clustering 聚类(unsupervised learning)

  • 降纬(高纬降低纬）
  • k-means(k均值)

feature scaling(特征缩放)

  • sklearn.preprocessing.MinMaxScaler formula: (sourceData - sourceDataMin)/(sourceDataMax-sourceDataMin)
  • 数据的新值范围(0-1)
  • http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html

  • module sklearn.preprocessing.MinMaxScaler

    from sklearn.preprocessing import MinMaxScaler

    data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] scaler = MinMaxScaler() print(scaler.fit(data)) MinMaxScaler(copy=True, feature_range=(0, 1)) print(scaler.data_max_) [ 1. 18.] print(scaler.transform(data)) [[ 0. 0. ] [ 0.25 0.25] [ 0.5 0.5 ] [ 1. 1. ]] print(scaler.transform([[2, 2]])) [[ 1.5 0. ]]

Text Learning

feature selection

PCA

Cross Validation