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import numpy as np import matplotlib.pyplot as plt a = np.arange(-10, 10, 0.1) s = 1.0 / (1.0 + np.exp(-a)) plt.plot(a, s) plt.show() これは$(-\infty, \infty)$の区間の実数を$(0, 1)$に写像する関数なので，あるデータ点${\bf x}$を$f({\bf x}) = {\bf w}^{\rm T}{\bf x} + b$によって1次元に写像したあと，シグモイド関数に通した値$\sigma(f({\bf x}))$は，$(0,1)$の値をとる確率値であると考えることができるようになる． 今，データ点${\bf x}$にはラベルとして0か1のどちらかが対応しているとする．このとき，${\bf x}$のラベルが1である確率と0である確率は，そ
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With this script you could have achieved the 13th place in the Rossmann Store Sales competition on Kaggle. No external data is used. If you would add Google Trends daily searches for 'Rossmann', State data and Weather data per State, you can get to the 7th place with just one model. But because that model would be useless for real world usage, that data is kept out of this model. import numpy as n
iPython-Notebooks / Comparing machine learning classifiers based on their hyperplanes or decision boundaries.ipynb / Jupyter Notebook Viewer
from pykalman.classifier import GenerativeBayes import pandas as pd import numpy as np rnorm = np.random.normal from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier from sklearn.qda import QDA from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from skle
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This is our ipython notebook which details our work in building a classifier on the avazu click through rate prediction. %matplotlib inline import numpy as np import matplotlib.pyplot as plt import pandas as pd import brewer2mpl from matplotlib import rcParams #colorbrewer2 Dark2 qualitative color table dark2_cmap = brewer2mpl.get_map('Dark2', 'Qualitative', 7) dark2_colors = dark2_cmap.mpl_colors
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#We scale/center all the data together here. Technically this is data peaking since we should "train scaler on test data" X = preprocessing.scale(log_ftr(items[features])) TEST_FRAC = 0.15 VALID_FRAC = 0.15 items_good = pd.DataFrame(np.hstack([items["State"].values[:, np.newaxis], X]), columns=["State"] + features) make_int = sorted(items_good["State"].unique()).index items_good["State"] = items_g
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