classLimeTabularExplainer(object):"""Explains predictions on tabular (i.e. matrix) data.For numerical features, perturb them by sampling from a Normal(0,1) anddoing the inverse operation of mean-centering and scaling, according to themeans and stds in the training data. 对于连续特征,standardsc...
Principal Components Analysis(PCA) is a well-knownunsuperviseddimensionalityreductiontechnique that constructsrelevantfeatures/variables through linear (linear PCA) or non-linear (kernel PCA)combinationsof the original variables (features). In this post, we will only focus on the...
As everything else in AI and deep learning, this is art and needs experiments. 3.8.2. Eigen portfolio with PCA # We want the PCA to create the new components to explain 80% of the variance pca = PCA(n_components=.8) x_pca = StandardScaler().fit_transform(vae_added_df) principal...
preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(1719) Note: The purpose of this section (3. The Data) is to show the data preprocessing...
Since the popular classes like theStandardScalerand thePCAare from the same Scikit Learn package, it is much easier to use them cohesively and process datasets efficiently. Despite the numerous operations required for a given task, users will find it easy to perform them with readable code. ...
As everything else in AI and deep learning, this is art and needs experiments. 3.8.2. Eigen portfolio with PCA # We want the PCA to create the new components to explain 80% of the variance pca = PCA(n_components=.8) x_pca = StandardScaler().fit_transform(vae_added_df) principal...
As everything else in AI and deep learning, this is art and needs experiments. 3.8.2. Eigen portfolio with PCA # We want the PCA to create the new components to explain 80% of the variance pca = PCA(n_components=.8) x_pca = StandardScaler().fit_transform(vae_added_df) principal...
As everything else in AI and deep learning, this is art and needs experiments. 3.8.2. Eigen portfolio with PCA # We want the PCA to create the new components to explain 80% of the variance pca = PCA(n_components=.8) x_pca = StandardScaler().fit_transform(vae_added_df) principal...
preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(1719) Note: The purpose of this section (3. The Data) is to show the data ...
preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(1719) Note: The purpose of this section (3. The Data) is to show the data ...