title: How to limit the CPU usage when applying sklearn.Linear_model.LinearRegression?
body: <p>I need to limit the CPU usage for the following command since it's using 100% of the CPU.</p>
  
  <pre class="lang-py prettyprint-override"><code>    from sklearn.linear_model import LinearRegression
      model = LinearRegression(fit_intercept = True, n_jobs = 1)
      model.fit(df_x0, df_y0)
      model.predict(df_x1)
  </code></pre>
  
  <p>I have set <code>n_jobs == 1</code>, and I did not use multiprocessing, but still it kept CPU fully occupied for all kernels and <code>df_y0</code>.
  <code>ndim == 1</code>, I learned that the <code>n_jobs</code> would not be effective if so.</p>
  
  <p>Can anyone tell me why it's using 100% of the CPU, and how to solve it in python? </p>
  
  <p>Python 3.7,
  Linux.</p>