9566

- nunique() = number unique() df[series == series.index[0]]

plt.style.use('defalut') 'fivethirtyeight' # 스타일시트=도화지+기본값 서브 플롯 간 간격을 변경 plt.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, wspace=0.2, hspace=0.35) plt.xlabel('Poverty Level'); plt.ylabel('Count'); plt.xticks([x - 1 for x in poverty_mapping.keys()], list(poverty_mapping.values()), rotation = 60) plt.title('Poverty Level Breakdown'); kdeplot vs histogram vs distplot : kdeplot은 연속형,..

!pip install plotly==3.8.1 # Let us load in the relevant Python modules import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import plotly.offline as py py.init_notebook_mode(connected=True) import plotly.graph_objs as go import plotly.tools as tls import warnings from collections import Counter from sklearn.feature_selection import mutu..

accuracy_score sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None) accuracy_score 파라미터 y_true1d array-like, or label indicator array / sparse matrix y_pred1d array-like, or label indicator array / sparse matrix normalizebool, default=True sample_weightarray-like of shape (n_samples,), default=None

make_scorer sklearn.metrics.make_scorer(score_func, *, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs) make_scorer 파라미터 score_funccallable greater_is_betterbool, default=True needs_probabool, default=False needs_thresholdbool, default=False **kwargsadditional arguments